{
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   "cell_type": "code",
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   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
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    {
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       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
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       "      <th>...</th>\n",
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       "      <td>29510123200</td>\n",
       "      <td>1232</td>\n",
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       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
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       "      <td>2152098</td>\n",
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       "      <td>510</td>\n",
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       "      <td>29510125500</td>\n",
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       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>1099352</td>\n",
       "      <td>0</td>\n",
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       "      <td>224</td>\n",
       "      <td>224</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>POLYGON ((-90.21128 38.62764, -90.21126 38.627...</td>\n",
       "    </tr>\n",
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       "      <td>29</td>\n",
       "      <td>101</td>\n",
       "      <td>980000</td>\n",
       "      <td>29101980000</td>\n",
       "      <td>9800</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
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       "      <td>17306316</td>\n",
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       "      <td>POLYGON ((-93.59588 38.73159, -93.59583 38.732...</td>\n",
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       "      <td>29</td>\n",
       "      <td>101</td>\n",
       "      <td>960900</td>\n",
       "      <td>29101960900</td>\n",
       "      <td>9609</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>707732249</td>\n",
       "      <td>3270288</td>\n",
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       "      <td>POLYGON ((-94.12917 38.65957, -94.12889 38.664...</td>\n",
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       "<p>5 rows × 345 columns</p>\n",
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      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        29        510    123200  29510123200   1232  Census Tract   G5020   \n",
       "1        29        510    124600  29510124600   1246  Census Tract   G5020   \n",
       "2        29        510    125500  29510125500   1255  Census Tract   G5020   \n",
       "3        29        101    980000  29101980000   9800  Census Tract   G5020   \n",
       "4        29        101    960900  29101960900   9609  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S    1113329         0  ...        0        0        0        0   \n",
       "1          S    2152098    578571  ...        0        0        0        0   \n",
       "2          S    1099352         0  ...      224      224        0        0   \n",
       "3          S   17306316     48221  ...        0        0        0        0   \n",
       "4          S  707732249   3270288  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0       10        0        0       10   \n",
       "2        0       60        0        0       60   \n",
       "3        0        0        0        0        0   \n",
       "4        0        0        0        0        0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-90.22163 38.61160, -90.22153 38.612...  \n",
       "1  POLYGON ((-90.22765 38.58271, -90.22753 38.583...  \n",
       "2  POLYGON ((-90.21128 38.62764, -90.21126 38.627...  \n",
       "3  POLYGON ((-93.59588 38.73159, -93.59583 38.732...  \n",
       "4  POLYGON ((-94.12917 38.65957, -94.12889 38.664...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/mo_pl2020_t.dbf\")\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fc0e15f-770c-4afb-90ab-20a5fd5b6a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0. :  #excluding linestrings\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "def r3(x):\n",
    "    y = round(x,3)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1654 popn tracts for MO\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MO\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "tractGEO_ID = tractPopFile['GEOID20']  #***CHECK THAT THIS IS RIGHT\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
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       "      <th>STATEFP</th>\n",
       "      <th>COUNTYFP</th>\n",
       "      <th>NAME</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
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       "      <td>Commerce</td>\n",
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       "      <td>0</td>\n",
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       "      <td>29</td>\n",
       "      <td>043</td>\n",
       "      <td>West Finley</td>\n",
       "      <td>1646</td>\n",
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       "      <td>41</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1642</td>\n",
       "      <td>519</td>\n",
       "      <td>...</td>\n",
       "      <td>1664</td>\n",
       "      <td>437</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>1652</td>\n",
       "      <td>445</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-93.26533 37.04093, -93.26521 37.040...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>175</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>549</td>\n",
       "      <td>128</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>518</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>546</td>\n",
       "      <td>115</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>543</td>\n",
       "      <td>122</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((-92.56115 39.42815, -92.56035 39.428...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29</td>\n",
       "      <td>165</td>\n",
       "      <td>Barry East</td>\n",
       "      <td>1206</td>\n",
       "      <td>1386</td>\n",
       "      <td>42</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>1237</td>\n",
       "      <td>1353</td>\n",
       "      <td>...</td>\n",
       "      <td>1334</td>\n",
       "      <td>1189</td>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1268</td>\n",
       "      <td>1235</td>\n",
       "      <td>73</td>\n",
       "      <td>24</td>\n",
       "      <td>POLYGON ((-94.60068 39.26755, -94.60070 39.266...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>135</td>\n",
       "      <td>California 3</td>\n",
       "      <td>639</td>\n",
       "      <td>210</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>651</td>\n",
       "      <td>201</td>\n",
       "      <td>...</td>\n",
       "      <td>664</td>\n",
       "      <td>172</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>652</td>\n",
       "      <td>182</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-92.57752 38.62075, -92.57742 38.624...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP COUNTYFP          NAME  G20PRERTRU  G20PREDBID  G20PRELJOR  \\\n",
       "0      29      201      Commerce         295          75           2   \n",
       "1      29      043   West Finley        1646         528          41   \n",
       "2      29      175    Huntsville         549         128           8   \n",
       "3      29      165    Barry East        1206        1386          42   \n",
       "4      29      135  California 3         639         210          18   \n",
       "\n",
       "   G20PREGHAW  G20PRECBLA  G20GOVRPAR  G20GOVDGAL  ...  G20SOSRASH  \\\n",
       "0           4           0         294          81  ...         296   \n",
       "1           4           2        1642         519  ...        1664   \n",
       "2           1           1         518         153  ...         546   \n",
       "3          15           7        1237        1353  ...        1334   \n",
       "4           3           1         651         201  ...         664   \n",
       "\n",
       "   G20SOSDFAL  G20SOSLFRE  G20SOSGLEH  G20SOSCVEN  G20TRERFIT  G20TREDENG  \\\n",
       "0          65           5           2           2         290          73   \n",
       "1         437          54          16          19        1652         445   \n",
       "2         115          12           6           5         543         122   \n",
       "3        1189          62          19          11        1268        1235   \n",
       "4         172          12           5           6         652         182   \n",
       "\n",
       "   G20TRELKAS  G20TREGCIV                                           geometry  \n",
       "0           8           2  POLYGON ((-89.44957 37.06921, -89.44957 37.068...  \n",
       "1          63           6  POLYGON ((-93.26533 37.04093, -93.26521 37.040...  \n",
       "2           6           7  POLYGON ((-92.56115 39.42815, -92.56035 39.428...  \n",
       "3          73          24  POLYGON ((-94.60068 39.26755, -94.60070 39.266...  \n",
       "4          19           3  POLYGON ((-92.57752 38.62075, -92.57742 38.624...  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/mo_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64b29ecf-f629-42ef-9d21-66aec1af8ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#DRAFT 7/8/22 - define a function to compute the HD-expected avg and std devn of a demographic property\n",
    "# ... for a geographic collection of people, across a coherent set of home districts\n",
    "def get_sd(t, property, tractPop, tractGeom, tractArea,tractUse, avgDistrictPop):\n",
    "    nTrax = len(tractPop)\n",
    "    propertyAvg = 0.\n",
    "    propertyVar = 0.   #stop here for now\n",
    "    for tt in range(nTrax):\n",
    "        intersxn = tractGeom[t].intersection(HDPoly[tt])\n",
    "        weight[tt] = intersxn.area/tractArea[t] * tractPop[tt]/avgDistrictPop / tractUse[t]\n",
    "        propertyAvg += weight * HDvGOP[tt]\n",
    "    for tt in range(nTracts):\n",
    "        HDvariance[t] += weight[tt] * (HDvGOP[t] - HDvGOP[tt])**2\n",
    "        drawnVariance[t] += weight[tt] * (drawn_vGOP[t] - HDvGOP[tt])**2\n",
    "        \n",
    "        HDsd[t] = HDvariance[t] ** 0.5\n",
    "        drawn_sd[t] = drawnVariance[t] ** 0.5\n",
    "        return sd[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3733 0.5783582064440145 2971750 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d04f4b91-1a9e-43fd-8ff0-34490d64c3a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8 enacted Districts for MO with avg district Pop 769364.125\n"
     ]
    }
   ],
   "source": [
    "#Now, pull in the enacted map data\n",
    "#enactedFile = gpd.read_file(\"enacted_map_files/OH_CD_2022.dbf\") #STANDARD FILE *****\n",
    "enactedFile = gpd.read_file(\"enacted_map_files/MO_GOPwins.dbf\")  #Geoff's GOP-favored Missouri example\n",
    "enactedFile.head()\n",
    "enactedFile = enactedFile.to_crs(tractPopFile.crs)  #MN's map file may be in wrong CRS\n",
    "enactedGeom = enactedFile['geometry']\n",
    "nDistricts = len(enactedGeom)\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "print(\"there are {0} enacted Districts for {1} with avg district Pop {2}\".format(nDistricts, STATE,r3(avgDistrictPop)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bbb53828-aefc-4cbd-8fbf-8b60dbc3497e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8 enacted Districts for MO\n"
     ]
    }
   ],
   "source": [
    "enactedFile = enactedFile.to_crs(tractPopFile.crs)  #for NY, enacted geom in wrong CRS\n",
    "enactedGeom = enactedFile['geometry']\n",
    "nDistricts = len(enactedGeom)\n",
    "print(\"there are {0} enacted Districts for {1}\".format(nDistricts, STATE) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "38b0b7f0-d1f1-443c-83a5-300713b70b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fixedGeom = enactedGeom[6].buffer(0.01)  #WTF is going on with district 7?\n",
    "plotPoly(fixedGeom)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0475a71-68da-47d1-bedb-bd4e379a6846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(enactedGeom[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a249af2c-17c9-4a92-90e2-d4dfec37dda7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHzSzG81st5ntMrNXN/tNVAujWI3KItIRRiay00sZ6ptTeRrY4u5jZlYAtpnZLcAngMvc/RYz2wpcDpxX4/irgG+4+6+ZWQ8w0KTYaypFTl6NyiLSASrDVmSkDaGeOZUdGEsXC+nN09uqdP1qYM/cY81sFfALwLvS5yoCxcUGfShhHGv6TBHpCKMZGscI6ishYGY54F7gZOBqd7/bzD4M3GpmV5BUPZ1T49CTgL3AZ83s5elzfMjdx2u8xiXAJQAbNmw4jLeSCCP1QxCRzjCSsYRQV92Ku0fuvhFYD2w2szOADwCXuvvxwKXAdTUOzQOvAP7e3c8ExoGPzvMa17j7JnffNDQ01Pg7SRXVD0FEOkRltrSMVBk1dOZ09xHgDuAC4J3AV9NNXwZqNSo/CTzp7nenyzeSJIiWCZUQRKRDZK1RuZ6rjIbMbDB93A+cD+wmaTM4N91tC/Dw3GPd/RngCTM7JV31euChxYc9vzBWxzQR6Qz7J0v05AL6MzKhVz1tCOuA69N2hAC4wd1vMrMR4CozywNTpPX/ZnYscK27b02P/13gC+kVRj8B3t3k9zBLKXJ1TBORjrB/ssiq/gJm2fgRW89VRg8AZ9ZYvw04q8b6PcDWquUdwKZFRdmAMNJVRiLSGZJeyu2fGKes635KJ1VGXfe2RKQLDY8XOXJFb7vDqOi6M2cYq6eyiHSGfeMl1qzIRoMydGFCiGPIKSGISAcYniiydkU2xjGCLkwIYRyTy0gDjYjIfNydfeNF1gwoIbRMFKP5lEUk8w5Mh4Sxq4TQSrFr6AoRyb5948mwbiohtFAYxWpDEJHMG04TgkoILRQ7BGpDEJGM2zeRlhCUEFon0tAVItIBhseTcYzWqsqoNdydkqqMRKQDVNoQ1A+hNWJPeir35bMxUJSIyHyGJ4oUcsYRvRq6oiWi2AHQyBUiknXlPghZGdgOuiwhxF5OCF31tkSkCw2PZ6uXMnRZQghVQhCRDjEyWWL3Mwd49sBUu0Op6KpTZ7nKSJedikjWla8u+v5jI+0NpEpXJYS4UkJQQhCRbPvP57wQyM70mVDfFJp9ZvY9M7vfzHaa2WXp+o1mdpeZ7TCz7WZWa07l8nPkzOw+M7upmcEf/DqtfHYRkeYpz6c8OJCdhFDP9U7TwBZ3HzOzArDNzG4BPgFc5u63mNlW4HLgvHme40PALmBVE2KeV7m1Pi0oiIhkVqWncid1TPPEWLpYSG+e3son+NXAnlrHm9l64JeBaxcd7QLKJQR3ZQQRybYslhDqakNIq3x2AM8Ct7v73cCHgU+Z2RPAFcAfz3P4p4E/BOLFBruQcmOy8oEcyhNPPMHrXvc6Tj31VE4//XSuuuqqdocky9C+8SIDPTn6CtnpSFtXQnD3yN03AuuBzWZ2BvAB4FJ3Px64FLhu7nFm9ibgWXe/d6HXMLNL0raI7Xv37m3kPVSU25JjZQQ5hHw+z5VXXsmuXbu46667uPrqq3nooYfaHZYsM8MT2ZocBxq8ysjdR4A7gAuAdwJfTTd9GajVqPzzwJvN7FHgS8AWM/uneZ77Gnff5O6bhoaGGgmrIlAbgtRh3bp1vOIVrwBg5cqVnHrqqTz11FNtjkqWm5GJbM2nDPVdZTRkZoPp437gfGA3SZvBueluW4CH5x7r7n/s7uvd/QTgYuCb7v6bzQm9VqzJvUoIUq9HH32U++67j1e96lXtDkWWmeGMTZ8J9V1ltA643sxyJAnkBne/ycxGgKvMLA9MAZcAmNmxwLXuvrVFMc/LKLchKCHIwsbGxrjooov49Kc/zapVLb0ATuQgIxNFNqwdaHcYsyyYENz9AeDMGuu3AWfVWL8HOCgZuPsdJNVNLRNUrjJq5atINyiVSlx00UW84x3v4C1veUu7w5FlKCkhdFiVUSdRG4LUw91573vfy6mnnsrv//7vtzscWYbCKGZ0KszUbGnQZQlBbQhSj+9+97t8/vOf55vf/CYbN25k48aN3Hzzze0OS5aRkcl0trSMJYTszMzQBGaGmRKCHNprXvMatTNJW42kvZQHM9ao3FUlBABDbQgikm1ZnE8ZujAhBGY4yggikl37KiUENSq3VGCmRmURybR940lCyFobQtclBLUhiEjW7UsHtstax7SuTAjKByKSZcPj0/QVAvp7sjOwHXRhQgjMdAWJiGTa8HiJI1f0tjuMg3RlQlAbgohk2fD4dObaD6ALE4LaEEQk64bHi0oIS0H9EEQk654fL3KkEkLrBYHaEEQk21RCWCKGBrcTkeyaKkVMFCPWHqGEsCTKg9yJiGTN8+VOaRnrgwBdmhBERLJqeCybvZShCxOCaotEJMueH58G4MhOrDIysz4z+56Z3W9mO83ssnT9RjO7y8x2mNl2M9tc49jjzexbZrYrPfZDrXgTB73uUryIiMhhGK6MY5S9jmn1zIcwDWxx9zEzKwDbzOwW4BPAZe5+i5ltBS4HzptzbAj8V3f/vpmtBO41s9vd/aEmvgcRkY4xnNGB7aCOEoInxtLFQnrz9FaemXw1sKfGsU+7+/fTxweAXcBxTYhbRKQjPT9epJAzVvVlb36yuiIysxxwL3AycLW7321mHwZuNbMrSBLLOQs8xwnAmcDdiwl4IVHkBIEqjUQkm4bHiqwZ6MEyeDlkXQnB3SNgo5kNAl8zszOAS4BL3f0rZvY24Drg/FrHm9kRwFeAD7v76Dz7XJI+Jxs2bGj0fVRMhzG9+WyNICgiUvb8eJHY4XN3PkohF1DIBaxdUeC8lxzd9h+zDZVZ3H3EzO4ALgDeCZQbib8MXFvrmLTd4SvAF9z9q4d47muAawA2bdp0WBcLxbFTjGJ68l138ZSIdIm1Kwo8NzbNx/5156z1N//eaznt2FXzHLU0FkwIZjYElNJk0E9SCvgkSZvBucAdwBbg4RrHGknJYZe7/1UT466pGMUA9CohiEhGffKin+NP33QapTCmFDk3PbCH//Fvu+gttP+8VU8JYR1wfdqOEAA3uPtNZjYCXGVmeWCKtLrHzI4FrnX3rcDPA78F/MDMdqTP99/c/ebmvo1EKU0IPbn2f7AiIrWYGav6ZuZSzqXVRFnoubxgQnD3B0gag+eu3wacVWP9HmBr1T5LVilWDNOEoBKCiHSIfeNFzGBVf2HhnVusq86cpShpeiiohCAiHWLfRInV/YVKSaGduurMWa4yyufa/8GKiNRj30QxE9VF0GUJoTwNQi6D1/eKiNSyb6LI4ED7q4ugyxJClGaEoKvelYh0s33jJdaohNB8UTozTqASgoh0iJGJImsyMq5RVyWE8tSZSggi0imGJ4qsUZVR85WrjLLQWi8ispDJYsRUKVYJoRXi5CIjlRBEpCPsm0iGwlYbQgvElSqjNgciIlKHmYSgKqOmi1VlJCIdZGSiBGSnhJC9GRoWQVcZibTOzT94mi/c/Ri9+Ry9+SC95ejJB+QCo5Az8rmAQpDc53NGPjDyQVDZlg8sXZ+uC5L9Crmq56haVz4+n0uOK8zZPx9YJucVqFe5hDCohNB8aT5o+5jiIt3oa/c9xX2Pj/CioSOYKkVMhzFTpYhSFBPGThg5YRxXhpBZKuUkU04WuUoCqrEuTVblxPP48AQ/fW6cE49awRnHrSYfWCXRBOV7S9bl0seBUXlcvT4XgGGYUdkvSBNWYMm2IN1G8o97Hh0GYHUGxjGCrksIakMQaZV940U2Hj/IP7/v7EPu5+5EsSdJInbCKEkSYRwTRk4piolir6wrRck+UeyUauxfeY70PoycUhwTRTP7h/Gc561aV3mOqtefKsWEUchPnxsHqNyHcUwcJ/fl9xDFThw7kTuxU3nsTcp7K3vzrFmhhNB05SojDV0h0nzDE0VOPWbhCVzM0mqhLp+4sJz4InfiOPlBGrsnE85XLcee7Bs7ODNJJXkOWD1QyMwsj12VEMolhE6uUxTJqpGJUmZ+yWZBJfG1O5Am6q6rjNJ+CLrKSKS5otiTIRYy0vgprdFdCaFy2WmbAxHpMqOTJWLPzuWR0hoLnjrNrM/Mvmdm95vZTjO7LF2/0czuMrMdZrbdzDbPc/wFZvZDM3vEzD7a7DdQLVKVkUhLlC+PXJuRIRakNer5LT0NbHH3lwMbgQvM7GzgcuAyd98IfCxdniWdh/lq4JeA04C3m9lpzQn9YOXB7dSoLNJcM9fLqw2hmy2YEDwxli4W0punt/IlB6uBPTUO3ww84u4/cfci8CXgwkVHPY9IYxmJtMS+8aRHrUoI3a2uBvL0l/69wMnA1e5+t5l9GLjVzK4gSSzn1Dj0OOCJquUngVfN8xqXAJcAbNiwod74Z4k1QY5ISwxnbBA2aY26Tp3uHqVVQ+uBzWZ2BvAB4FJ3Px64FLiuxqG1fqrX7M7h7te4+yZ33zQ0NFRX8HPFGrpCpCX2jacJQSWErtbQb2l3HwHuAC4A3gl8Nd30ZZLqobmeBI6vWl5P7aqlpigPXaHLTkWaa99EiZ5cwIqebHSgktao5yqjITMbTB/3A+cDu0lO7Oemu20BHq5x+D3Ai83sRDPrAS4Gvt6EuGuKNHSFSEvsG08mgtcVfN2tnjaEdcD1aTtCANzg7jeZ2QhwlZnlgSnS+n8zOxa41t23untoZh8EbgVywD+6+85WvBFQlZFIqwxPFNWgvAwsmBDc/QHgzBrrtwFn1Vi/B9hatXwzcPPiwqyP5kMQaQ31Ul4euup6HM2HINIaw+NFjWO0DHRVQnDNhyDSEiMTJZUQloGuSghqVBZpvjh29qkNYVnoqoQQa+gKkaYbnUoGtsvKNI/SOt00lHflKiNdGieysB/97AATxagybWSuavrIXDqXcRDAnpEpANaqDaHrdVdCUMc0kbrsenqUX7rqOw0dc+SK3hZFI1nRVQlh5iqjNgciknHP7E9+9f/3N53GC9cOVOYOnjuXcBg7URTTW8hx9klHtjlqabWuTAgqIYgc2uhUMnrpeacM8aKhI9ocjWRFVzUqF9Pxr3vyXfW2RJpudCoEYGVfV/0mlEXqqjPndJgmBM2hKXJIo5NJCWFVnxqKZUZXnTmLYUxPLtBVRiILGJ0q0ZMP6Cto9FKZ0VUJoRTFFHJKBiILGZ0MVTqQg3RVQiiGsdoPROowOlViVb/aD2S2rjp7JiWErnpLIi1xYCpkpUoIMkdXnT2LkUoIIvUYnSyxSlcYyRxd9RdRilxXGMmyNVWK2HtgOhl2ImcUgoBc+T4wCjmrXHAxOlXiuDX9bY5Ysqa7EkKoKiNZvt792Xu48yfPH3Kf8nhFxTDmVSeq57HMtmBCMLM+4NtAb7r/je7+cTP7F+CUdLdBYMTdN9Y4/lLgtwEHfgC8292nmhL9HKUoppDXVUayPD0+PMErT1jDRa9YTxg7YRQn99WPI6cUx8Sxc9FZ69sdsmRMPSWEaWCLu4+ZWQHYZma3uPuvl3cwsyuB/XMPNLPjgN8DTnP3STO7AbgY+D9NiX6OohqVZRnbP1nijae/gIs3b2h3KNKh6plT2YGxdLGQ3ry83ZJKybcBWw7xGv1mVgIGgD2LCfhQiqoykmUqjGLGpkNW9+vKITl8dZ09zSxnZjuAZ4Hb3f3uqs2vBX7m7g/PPc7dnwKuAB4Hngb2u/tt87zGJWa23cy27927t8G3kShFsRqVZVnanw5FoWkuZTHqOnu6e5S2D6wHNpvZGVWb3w58sdZxZrYGuBA4ETgWWGFmvznPa1zj7pvcfdPQ0FADb2FGKXL1VJZlaSRNCIMDKiHI4Wvo57S7jwB3ABcAmFkeeAvwL/Mccj7wU3ff6+4l4KvAOYcb7EJK6ocgy1S5hKAqI1mMBc+eZjZkZoPp436Sk/zudPP5wG53f3Kewx8HzjazgbSt4fXArkVHPQ81KstytX9CCUEWr56z5zrgW2b2AHAPSRvCTem2i5lTXWRmx5rZzQBpW8ONwPdJLjkNgGuaFPtB1IYgy9XIZBGAQbUhyCLUc5XRA8CZ82x7V411e4CtVcsfBz5++CHWrxS6SgiyLI2kJYRBlRBkEbrq7BnGTqDpM2UZKieEVUoIsghdNXQFgObGkeWo3Kj8ps9sI58OT7GiN8f//JWXccJRK9ocnXSKrksIIsvR1pet49kDU4SRE8XOvoki333keXbuGVVCkLopIYh0gc0nrmXziWsry/c8Osxb/+FOVmqIa2lAV7UhiEjiwFRShaSEII1QQhDpQgemQgDNiiYNUUIQ6UKjaULQrGjSCCUEkS40U2WkEoLUTwlBpAsdmArJB0ZfQV9xqZ/+WkS60IGpEiv78pU5lEXqoYQg0oUOTIWqLpKGKSGIdKEkIahBWRrTdX8xTwxPcNvOZ8jnjMCMfBAQBJAzO3hdYOSD2etm7V9jXS4wFcMl88pVRiKN6Kq/mMGBAt95+Dm+8/BzLX2dwJidLCxJLrkgIFcjgVRvm72ualvlOWavC9KkVVlvM89VHsgvn+5fCIxczmaWc1ZJeuXlfBCQT/fJ55Jj8rmAXGCV7YVcsi7ZZ2ZdLjD6CznyGlE28w5MhRy/dqDdYUiH6aqE8JX3n8NTI5PE7oRxMqZL+VZeF8dztvnMusq2GutiT/avta58K2+L5qyPDrGuVIqJ4uig9fXGCVT2WSqBQU8+oDefoycf0JML6M0H6brg4G2F5L68fs1Agff9wkn0FXJLFvNyoyojORxd9RezeqDA6mU6p6xXJawwdqLICeOYKHZK6XIpjgkjpxTF6b4xpcgJ033L96VZy04YzaybKsUUw5hiFDNdipL7MLkVq24TxZCRyZnl6cr6iMlSxNkvOpJXnrB24Tcmh2V0qsTK3q76essS0F9Ml7C0jSSf8R/dtz/0M973ue30q3TQMnHsjE3rKiNpXD1zKveZ2ffM7H4z22lml6Xr/8XMdqS3R81sxzzHD5rZjWa228x2mdmrm/wepIOMTydDKgz0KCG0yngxxB1W9ev3njSmnr+YaWCLu4+ZWQHYZma3uPuvl3cwsyuB/fMcfxXwDXf/NTPrAdTStYyNF5OEsELVGS2jge3kcNUzp7IDY+liIb1VWjAtuQbzbcCWucea2SrgF4B3pc9VBIqLDVo618R0BKiE0EozCUFJVxpT1/WDZpZLq4SeBW5397urNr8W+Jm7P1zj0JOAvcBnzew+M7vWzGpO32Rml5jZdjPbvnfv3sbehXSMcglhoEcnq1bRwHZyuOpKCO4euftGYD2w2czOqNr8duCL8xyaB14B/L27nwmMAx+d5zWucfdN7r5paGio3vilw4xPh/QXcuQCde5rFZUQ5HA19Bfj7iNmdgdwAfCgmeWBtwBnzXPIk8CTVSWKG5knIcjyMB3GTJYiXvKnt8zqcJev7niXdoYLDIKq3uG5IFlObunjwCodA2ttK3cETB5TY126f631lXVztlfuqbHvnOMOWmezOvzlc0ah3DGw3GmweltVR8J6e8iPpiUEzYUgjVrwL8bMhoBSmgz6gfOBT6abzwd2u/uTtY5192fM7AkzO8Xdfwi8HnioSbFLB/qts1/Iyr587U6CczvepZ3/kg6AaV8Ld2Kf6XcRuxPHEEbxQdvK25N7aqyb2VbdobB6fZbkqpLK3FtlCJac8djzEwAc0asqI2lMPT8h1gHXm1mOpIrpBne/Kd12MXOqi8zsWOBad9+arvpd4AvpFUY/Ad7dlMilI734BSv5g198abvDqIv7TCKp9EpPe4xXEknN5JMksPmOK3cWDKM47fh3cMfAKE46EJYTYylKttfqxZ48jtPXjHnZcas5emUfL1jV2+6PUDpMPVcZPQCcOc+2d9VYtwfYWrW8A9h02BGKtImZVcaYElkONEqZiIgASggiIpJSQhAREUAJQUREUkoIIiICKCGIiEhKCUFERAAlBBERSZl7xvrnA2a2F3isCU91FPBcE56nFRTb4VFsjctqXKDYDlet2F7o7osaGTSTCaFZzGy7u2eyl7RiOzyKrXFZjQsU2+FqVWyqMhIREUAJQUREUt2eEK5pdwCHoNgOj2JrXFbjAsV2uFoSW1e3IYiISP26vYQgIiJ1UkIQERGgCxKCmb3czO40sx+Y2f81s1Vztm8wszEz+8ghnuN3zeyHZrbTzC7PUmzpfh8xMzezo7ISm5l9ysx2m9kDZvY1MxvMUGxrzex2M3s4vV/TyrjMbLOZ7Uhv95vZr85z/EYzuyvdb7uZbW5GXM2ILd13Sb8HjcSW7r9k34MG/k+X/HvQQGyNfw/cvaNvwD3Auenj9wB/Pmf7V4AvAx+Z5/jXAf8O9KbLR2cltnSf44FbSTrqHZWV2IA3Avn08SeBT2YotsuBj6aPP9qs2OaLCxio+izWAc+Wl+ccfxvwS+njrcAdrf7MGohtyb8H9caWbl/S70EDn9uSfw8aiK3h70HHlxCAU4Bvp49vBy4qbzCzXyGZx3nnIY7/APCX7j4N4O7PZig2gL8G/hBoduv/omJz99vcPUwX7wLWZyU24ELg+vTx9cCvtDIud5+o+iz6mP//yoFyaWc1sKdJcTUjtiX/HjQQGyzx96De2NrxPWjgc2v4e9ANCeFB4M3p47eS/JLAzFYAfwRctsDxLwFea2Z3m9l/mNkrsxKbmb0ZeMrd729iTE2JbY73ALdkKLYXuPvTAOn90a2MK43tVWa2E/gB8P6qL2y1DwOfMrMngCuAP25SXM2Ibcm/B/XG1o7vQb2xzbEk34MGYmv4e5BfdMhLwMz+HTimxqY/IflP+Bsz+xjwdaCYbrsM+Gt3HzM75CTpeWANcDbwSuAGMzvJ03JWu2Izs4H0Od5YTxxLGduc1/gTIAS+kLXYDsdhxoW73w2cbmanAteb2S3uPjXnOT4AXOruXzGztwHXAednJLZ2fA8WjK2N34N6P7fyayzl96Ch2BrSrPquLNxIfuV8L338HeDR9DYCDAMfrHHMN4DzqpZ/DAy1OzbgZSR1g+X9QuBx4Jh2x1Z13DuBO4GBjP2f/hBYlz5eB/ywlXHV2PYtYFON9fuZ6ftjwGirP7MGYlvy70E9sbXre1Dv55ZuW9LvQQP/pw1/D1oS/FLeSBu/SKq/Pge8p8Y+f8b8DZDvBz5R9aE/Uf7Stju2Ofs9SnMb0xb7uV0APNSik8ZiY/sUsxvTLm9lXMCJzDTyvZCkbeCg/ytgV/mkC7weuLfVn1kDsS3596De2OY815J8Dxr43Jb8e9BAbA1/D7qhDeHtZvYjYDfJB/PZhQ4ws2vNrDxS4D8CJ5nZg8CXgHd6+glmILZWWmxsfwusBG5PL3/7hwzF9pfAG8zsYeAN6XIr43oNcL+Z7QC+BvyOuz9XI673AVea2f3AXwCXNCmuZsTWju9BvbG10mJja8f3oN7YGv4eaOgKEREBuuMqIxERaQIlBBERAZQQREQkpYQgIiKAEoKIiKSUEEREBFBCEBGR1P8HY1aIbwtfFgIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "enactedMAP = enactedGeom[1]\n",
    "for d in range(2,nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "    enactedMAP = enactedMAP.union(enactedGeom[d])\n",
    "    plt.text(enactedGeom[d].centroid.x, enactedGeom[d].centroid.y,d)\n",
    "    plt.show()\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "TopologyException: Input geom 0 is invalid: Hole lies outside shell at -93.063777999999999 37.161780999999998\n"
     ]
    },
    {
     "ename": "TopologicalError",
     "evalue": "The operation 'GEOSUnion_r' could not be performed. Likely cause is invalidity of the geometry <shapely.geometry.multipolygon.MultiPolygon object at 0x7fe0ccfdc910>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTopologicalError\u001b[0m                          Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_134/4111642116.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnDistricts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m999\u001b[0m \u001b[0;34m:\u001b[0m     \u001b[0;31m#change -999 to a district number if there is one bad district\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0menactedMAP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menactedMAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menactedGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mplotPoly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menactedGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menactedGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0menactedGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcentroid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;34m\"\"\"Returns the union of the geometries (Shapely geometry)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'union'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[0;31m# Unary predicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n\u001b[1;32m     71\u001b[0m                     \"This operation could not be performed. Reason: unknown\")\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_topology\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m_check_topology\u001b[0;34m(self, err, *geoms)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mgeom\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgeoms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_valid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m                 raise TopologicalError(\n\u001b[0m\u001b[1;32m     38\u001b[0m                     \u001b[0;34m\"The operation '%s' could not be performed. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m                     \"Likely cause is invalidity of the geometry %s\" % (\n",
      "\u001b[0;31mTopologicalError\u001b[0m: The operation 'GEOSUnion_r' could not be performed. Likely cause is invalidity of the geometry <shapely.geometry.multipolygon.MultiPolygon object at 0x7fe0ccfdc910>"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic enacted\n",
    "enactedMAP = enactedGeom[0]\n",
    "plotPoly(enactedGeom[0])\n",
    "plt.show()\n",
    "plt.text(enactedGeom[0].centroid.x,enactedGeom[0].centroid.y,0+1)\n",
    "for t in range(1,nDistricts):\n",
    "    if t != -999 :     #change -999 to a district number if there is one bad district\n",
    "        enactedMAP = enactedMAP.union(enactedGeom[t])\n",
    "        plotPoly(enactedGeom[t])\n",
    "        plt.text(enactedGeom[t].centroid.x,enactedGeom[t].centroid.y,t+1)\n",
    "    #plt.show()\n",
    "\n",
    "plt.show()\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "020d6267-37ca-40ae-967d-7ea3316e727e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nf8LPZQPSePDMTKKcdouTNW16EDPA4mOcTLhpILef3Klq26a9xRYmUurEpcRFcecpnZk4dgjDO7dk/OyNjHluOuN+2kCZy2N1PHUELeAnoF2LGG4d4Zv/e3S3FP58fpbFiZQKjB6pzfnXtQN466ZBtImP4vEvV3Pa89NZvq3Q6miqGi3gJygmwvfVsmfbeOKjnRanUSqwRnZN5sPbh/LurSfh8RgufHUW93+4lHK3tsZDgRbwExQT4WBopyTenLmRCbM26qKyqlEa2rklX9wzgusGt+ejhdu44c35WsRDgBbwAHjmsj50bdWMRz9fxYinp/L50h1WR1Iq4BJjI3j0/CyeurgXczfkc9Ers5m6Rs+HsJIW8ABITYhm0h3D+PTOYbRuHsX/TVzC/uIKq2MpFRRXDUrn1Wv6U1jq4rb/5lBQoq91q2gBD6A+aQn0aNMct9cwfd0eFm7OtzqSUkFxdq82PH9FX9xew/yN+jq3ihbwABvSKQm7Tbhv4hIu+dccPlq4DfBNHrRw837mbdiHx6uroqjw1yctHvAt9K2sofOjBthl2Wmc1qMVP+8p5okvV/HgR0v5dMl2NuwpZntBKeD7CnrbiA7sKiyjb3oCMTpNrQpDkQ47GUkxLNcCbhltgQdBQkwEA9q34O1bTuLmYR3YX1JB55RmvHBFXzKSYnhv/hZGPzudq8fN47LX5lgdV6njdkq3FGb/vI/SCh2RYgVt+gVRbKSDh8/tcdi2Ae1b8O3KXRSXe3j+h3UUluqwQxW+Tu3WivGzNvHmrI3ceUpnq+M0OdoCb2BpiTHcOqIj6UnRADxxUS+LEyl1/IZ1TuLMrNa88MO6qrVjVcPRAm6RyjnEMpJirA2i1Amo8HgpKK3A4zVUuL1Wx2lyjlrARSRKROaLyFIRWSkij/m39xGROSKyXEQ+F5HmwY/bePzoPwGi8sCmUuHoiS9XM3dDPo+dn0VibITVcZqc+rTAy4HRxpg+QF/gTBEZDIwDfm+M6QVMBh4IWspGKNJh9//WL0EqfO32LzF4Zs82Fidpmo5aPYxP5aTXTv+PATKBGf7t3wOXBCVhIzWoQwsA2sRHW5xEqeP34JndcNiEF35YZ3WUJqlezT8RsYvIEiAP+N4YMw9YAZzv3+UyIK2W+44VkRwRydmzZ08AIjcObv/JPA6bruCjwlen5GZcO7g9783fctjiJrXxeI2eyBZA9RpGaIzxAH1FJAGYLCI9gZuBF0XkEeAzoMYJEYwxrwOvg29FnkCEbgyqXsNav1WYu25IeybM3sT8jfl0TmlW637jftrAP39YT6nLQ5uEKLqkxHFmVmvO6Nlap2I+Tsc0DtwYUyAi04AzjTHPAKcDiEhX4JzAx2u8quq2fqSpMNc+MYaEGCezcvdy9UnpNe6zeMt+Hv9yNSO6tKRX23i27S9lydYCHlyzjIc/XcHozBQu7JdKVmo8CTFO4qK0oNfHUQu4iCQDLn/xjgbGAH8XkRRjTJ6I2ICHgdeCnLVRqVz7WOu3CncOu41L+7dj3MyNZE5Zz92jOx+2uHfegTIe/XwVMRF2nru8L8lxkQAYY1i2rZBPlmzn86U7+Wblrqr7dEqO5aZhHbg8O40IPdBfq/q0wNsAb4mIHV+f+QfGmC9E5F4RudO/zyRgfLBCNkbib4M34JrSSgXNb0/PJL+4gue+X8feonLuGNWZ1vFRzN2wj7veXUxxuZvnrzhUvAFEhD5pCfRJS+CPZ3dn/sZ8dhSWkXewjG9W7OLhT1YwYfYm7hvThdN7tNZCXoOjFnBjzDKgXw3b/wn8MxihmoLKY5dereCqEYiOsPPs5X2IirDz3zmb+d/czaQnxrBpXwkdk2N559aTyGwdV+v9HXYbQzu3rLr+65M78eOaPP76xSruencxqfFR3Dm6M5cN0BZ5dToXikW0C0U1NiLCkxf14lcjO/LRwm2s313EpQPaceOwDjSLPLZSIyKc2r0VozJTmL4uj39OyeWPk1fw+owNfH73cJprHzmgBdxyRlvgqpFpnxTLb0/PDMhj2W3C6G6tOCUzhce/XM0bMzeyu7BMC7iffhexyKFx4PpfoNTRbNtfypyf95ESF0mn5NqHKjY12gK3iNvjL+B2HQiuVG2MMYyftYl/fLsWEd8C4jY9+a2KFnCLuDy+mduc2gJXqkbGGF76MZfnvl/HKZnJPH5RL9om6NQT1WkBt0jl6cTaAleqZpV93mO6t+L16wZoy7sGWsAt4tYCrlSdJi3axqCMRP5z/YDDTgxSh+j3d4toF4pSdRvZNZlFW/azeGuB1VFCllYPi7g9BpugXwuVqsVfLuhJm4Qofv/xMqujhCwt4BZxeb047PrnV6o28dFOrhyYzrrdRRwo08W/a6IVxCIutyFCC7hSdfo5r4jYCDvRTrvVUUKSVhCLGIxOBa5ULUorPPzt6zVMWrydq09Kx6mNnRrpKBSLGIMu5qBUDdweL7f9N4eZuXu5cmAa958RmNPyGyMt4BbS+q3ULz3+5Wpm5u7lbxf34spBNS8QoXz0e4lFjDE6tlWpIyzesp8JszcxrHOSFu960AJuEcOhKWWVUj4f5GwjJsLO69dlWx0lLGgBt4gxYNMKrlQVl8fL1yt2clqPVsQe4/zhTZUWcIvoKBSlDjcrdy8FJS7O7Z1qdZSwoQXcIl6jXShKVSoud/Pa9J+Ji3IwsmvLo99BAToKxTK+hXi0giu1vaCUW9/KYe2uAzxxUS8iHXrSTn1pAbeM0Ra4avLyDpZx4SuzKKvw8OaNAxmVmWJ1pLCiBdwixmj7W6lXp/5MfnEFn981nB6pza2OE3a0D9wiOgpFNXU7C0t5e+5mLs9up8X7OGkBt4jRLhTVxG3cU4zHazivj446OV5awC2iXSiqqfMvSoVdWzLHTQu4RXxnYuoLVzVdSc0iAMg7WG5xkvClBdwiXmOsjqCUpTomx+KwCSt2FFodJWxpAbeKnsijmrhIh52TOibyyeLtFJRUWB0nLGkBt4hBR6Eodf/pmewvdnHThAUs2rIfr1e/mR4LLeAW8U0na3UKpazVL70FL17VjzU7D3Lxq7M57+WZrN990OpYYUMLuEV0QR6lfM7s2Zo5D43m6Ut6s/tAOdeMm8euwjKrY4UFLeAWMUZHoShVKSEmgssHpvG/WwdRXO7m1v8uoKTCbXWskKcF3CLaAlfql7q1bs5LV/dj1Y4DnPvSTKauzbM6UkjTAm4Ro2fyKFWj0d1aMf6mQRgDN41fwDkv/sR1b8zjmxW7fO8bVeWoBVxEokRkvogsFZGVIvKYf3tfEZkrIktEJEdEBgU/buOho1CUqt3JXZP59r6R/OHsbrg8XpZtK+T2/y3k25W7rY4WUuozG2E5MNoYUyQiTmCmiHwN/AV4zBjztYicDTwNjApe1MbFGF2RR6m6RDhsjB3ZibEjO1Hm8tDnse/4w+TlbM0voX1SDCO7JhPlbNpzhx+1BW58ivxXnf4f4/+pnEIsHtgRlISNlNcLdpuWcKXqI8pp56Pbh9KyWQRPfLWasW8v5MJXZrFoy36ro1mqXvOBi4gdWAh0Bl4xxswTkfuAb0XkGXwfBENrue9YYCxAenp6IDI3Cm6v0S4UpY5Br3bxfHvfSApKXHy1YicvTlnP5a/N4YmLenJaj9as3FFIXJSTvmkJVkdtMHIsBwVEJAGYDNyNryhPN8Z8LCKXA2ONMWPqun92drbJyck5gbiNx03j57OvuILP7hpudRSlwlJhqYs731nEzNy9h23/+yW9uGJg42osishCY0z2kduPaRSKMaYAmAacCdwATPLf9CGgBzGPgUcXdFDqhMRHOxl/00CeurjXYdub0pqa9RmFkuxveSMi0cAYYA2+Pu+T/buNBtYHKWOj5PF6cWgfuFInxGm3cUZW66rrX90zggv7tQVg5Y5Cnv1uLcu3Nd7ZDuvTB94GeMvfD24DPjDGfCEiBcA/RcQBlOHv51b14/EabFrAlTphibERXNA3la+W72TzvmKS4yL5dMl2/v7NGlwew8tTc7l+cHt+f1Z3oiMaV+v8qAXcGLMM6FfD9pnAgGCEago8XoPTrudRKRUIj5zbg0Vb9vPrdxZVbRvdLYXHzs/i6W/X8taczewpKufeU7vSKTkWRyN57+mq9BZxew1RzvBqge8oKGXZtkIiHEJ8tJN+aS30W4QKCUnNIvnhNyczK3cvW/aVkNm6OYM7JiIiPHNZb9buOsBXy3fx1fJdtIhx8tJV/RnepaXVsU+YFnCLeMNwGOHDn6zgxzWH5qbolBzL4I5JRDhsRNhtVb+d1X5H+rc7q37LYfu3T4olPtpp4b9KNRaRDjuju7Wqcftndw0nN6+INbsO8uq0XK59Yx5nZLXi3lO70iO1eQ2PFh60gFvEY0zYncizr7iC/ukJ3H5yJ/4weQUHytx8s2IXFW4vFR7fz7FOVdE3LYFP7hwWnMBK+UU57fRsG0/PtvGc1bM1r8/YwJuzNvLdqp8Yd302p3b/ZeEPB1rALWIMhFn9pqTcTeeUZpye1ZrTqx35r2SMweM1vmJeWdTdXlwe47t+2DYvz363lgq314J/iWrKYiMd/N9pXbl5WAcGPP49Xy7bqQVcHRvfylHhVcEPlLn4esUulmwtqPFsNxHBYRccdhsxEUd/vFen5VJRqnM+K2s47ILba5i0eDs5m/eT3b4FT1/aO6wOcIZP0kYmHJdUG5iRCMA7czcH5PEq3F5W7zzAu/O2BOTxlDoWsZEOnrioJyd3TeZAmYtJi7dz04QF5BeHzwLLWsAtFG5dKC9f3Z8WMc6AzQD35/OyANh9QJfPUta45qT2vHXzIJY8cjp/v6QXs3L3MuDx7znj+Rk8+dVqyt0eqyPWSbtQLOI1BgmzLhSAkgoPMQE6GSKzdRwAkU5tRyjrXTEwnZ5t4/lhVR7T1+Xx+owNdG0Vx6UD2lkdrVZawC1iDMzZsI+LX52F0+4bZuewCw6bjQiH77fDLjhtNpz+605//7LTbsNpq7wsOGyC02EjymEnymknOsJGlNNOs0gHWanxARvt4vUayt3egLXAy/0HMJvS3BUqtGWlxpOVGs9JHRO58vW5JDWrx8EcC2kBt8i1g9szdW0ebo/B5fFSUuHG7TW4PAa3x4vb6xu54fZ6q/bx3e4b1VFff7kgi+uHZAQkc6nL93UyUC3wyq+nkQ5tgavQ4vL4GhdLtxZwSmaKxWlqpwXcIjcMzeCGoRnHdd/K4XrVC7rb46Xc7aXU5aG0wsO+4nJunpAT0GF6JRW+ghuo+STKXZUtcC3gKrQM6ZjE2b1a88IP67GLcPepXayOVCMt4GHo0HA9au3O2HOwHIAPcrYyb2O+v6vFhsN2aKifw3aoa8Ze2SVjE+z+rhuHv3umct8DZb4hf9EB60Lxt8Cb+LJYKvRs2FvM3oO+0SjP/bCO20Z2DMnl27SAN1ItYpyc1qMVuwrL2JpfcliL3VPZVeP14vEYXP5uGre3fl0zreOjApKxTFvgKkTNWLeH+ZvyadcimnN7p4bsa1QLeCPlsNv4z/W/WMCjTtW7ZtxeX7eMy2P8Bd/XB++wCWmJMQHJeOggZmi+OVTTlRjrO3j52rUD6Nk23uI0tdMCrqpU75ppCIcOYobeV1PVtA3r3JK4SAePfb6SiWOHhOysm9r0UZapbIFH6ThwFWJaNY/i4XO7s2DTfiYt3m51nFrpO0dZ5tAoFG2Bq9BzSf92DMpI5I+TlzPriIWTQ4UWcGWZQ6NQ9GWoQo/DbuPVa/uTnhjD9W/O5/OlO6yO9AvaB64sU9mF8sSXq4mPdlYNcWzXIoY7RnVCwm22L9XotGwWyaQ7hnLj+AX8YfJyRmUmExcVOguQaNNHWSYrtTk92zZn075icjbnMyt3L58v3ck/vl1bNeZcKavFRTl55NweHCxzM2lRaPWHawtcWSYrNZ4v7h5x2LbXpv/M375eg9OurW8VOnqkNifCbmPzvhKroxxGC7gKKS5/t4ozjCbVV41XQUkFS7YWMG3tHio8XgZ3TLQ60mG0gKuQ4vJ4EQFHiI67VU3H8m2FXDNublV3Xp+0BEZ2TbY41eG0gKuQUu7x4rTb9ACmslRhiYur/jOX5lEOXrmmPz1T42kRG3pTy+r3VBVSXG7fNLp5ukqPslDO5nyKyt387qxujOiSHJLFG7SAqxDTIsY3ROulH3MtTqKasv7pLWgTH8Ujn67k/g+XMm1tntWRaqQFXIWUu0Z3BnxLzilllRaxEUwcO4QOLWP5aOE2/vzZSqsj1UgLuAopIkJclENHoSjLpSfF8Mmdw0iMjaBHm+ZWx6mRvktUyHF5vEToFLMqRPRNS2DBpnyKy0Pv5DJ9l6iQ4/IYIrQFrkLEnad0Zl9xBY+GYDeKvktUSPF4fQtIaBeKChUD2rfgrlM68+HCbXwWYhNa6btEhZTK1cCdDh0HrkLHvad2oX96Ag9+tJT/zNiACZGD7FrAVUipLODahaJCicNu47VrBzCsU0ue+Gp1yExqpe8SFVJcHl/LRrtQVKhJaR7Ff67PJibCzuyf9wEw7qcNXP2fufzq7RzOfekntuY37GRXRz2VXkSigBlApH//j4wxfxaRiUCmf7cEoMAY0zdIOVUj5/Z42ZJfwr7iCkALuApNm/NLKKnwEBflK50f5Gxl3e6iqtsf/mQFb9yQjaOBXr/1mQulHBhtjCkSEScwU0S+NsZcUbmDiDwLFAYrpGr8nvp6DW/M3Fh1PTZSl1lToSclLpKMpBgmzN7EjHV7aNU8irYJHrYXlAIwfd0eLv7XbJ6/oi+dkpsFPc9RPyaMT+VHjNP/U9WDL75Zhy4H3gtKQtUk5B0sJzkukmcu68PzV/Th9B6trY6k1C/ERjr45r6RPH5hT1rHRzFnwz6aRTpIiHES7bTz8tX9WLvrIA9NWl51n8qlA4OhXrMRiogdWAh0Bl4xxsyrdvMIYLcxZn0t9x0LjAVIT08/sbSq0fJ4vcRFObh0QDuroyhVpyinnWsHt+fawe2ZujaP+z9YSmGpiwfP6Ma5vVNZtLmA8bM3cvd7i1mydT9b80u5IjuNpy7uhS3A0yTXq6PGGOPx92+3AwaJSM9qN19FHa1vY8zrxphsY0x2cnJozaWrQofXCxv2FPPxwm0cLHNZHUepejklM4U5D51Kzh/H8OtRnQC4cWgGfdMSmL9xH1lt4hnTPYWJOVtZvHV/wJ//mOYDN8YUiMg04ExghYg4gIuBAQFPppoUp//U+d9+uJS/VGRx/ZAMawMpVU8RDhtJzSKrrqcnxTD5jmFV1/83dzM/rM4jISbwU9IetQUuIskikuC/HA2MAdb4bx4DrDHGbAt4MtVkPPrZSj6vdoabXVfjUY3Il8t20rFlLB1bxgb8sevTAm8DvOXvB7cBHxhjvvDfdiV68FKdgN0HypgwexMi8I9L+xDltDG6W4rVsZQKiN0Hypi/KZ/bT+4YlFWmjlrAjTHLgH613HZjoAOppuNgmYu731sMwPu3DeakjkkWJ1IqcPKLK3jk0xXYRbhsQFpQnkPXxFSWeXvuZuZvzOemYRkM6hBaq30rdSKe+no1/56+AYBrTkonIwjdJ6AFXDWkdd/Cu5cDkFk2gXJ8B3VGdknWRYxVo/LNil20TYjm6Ut7MziI3yz1fGXVcFJ6YKKT2GZaUoGD03u04uZhHRjepaXVyZQKqB5tmrOzsJQXfljHnz5dEbTFILSAq4aTkIb8bgMzz5mKwcaj52fxyHk9dN4T1eic1ycVr4EFm/bz7rwtTF4cnNkL9Z2jGlxlwS4KwSWqlDpRb8/ZxB3vLDpsW7DmRdE+cNXg2iREAfD2nM3aAleNzoTZm+jYMpYPbh/CrsIy7Dahe5AWRdZ3jmpwnf2tkbfnbqbLH7/mT5+ssDiRUoEzqEMSG/YW8/78LfRsGx+04g1awJUFUppH8cRFh6bTcdh1BIpqPP56QRb90xN45rt1rNge3Fm2tQtFWeKjhdtIjovk2/tGkhgb+DkilAq2g2Uuopz2X3QBVni85PsXJlm3+yA928YHLYMWcNXgSis8rNpxgO5tmmvxVmFn5vq9PPr5SnLzimjZLJKJvxp82EHKv3y+is35Jfz2tK5c1K9tULNoF4pqcJEOG+2TYliytQC3fxFjpcLF7z5eRmmFh9+c1hWvMdz2Vg4FJb4Wt9dr+HrFLi7q15a7T+0S9BPUtAWuGpzNJjhsvrbDgTK3tsJVyCgqd/PEl6tZsCmfNvFRpCfGcE7vNgzt5DvZbP3ug2wvKOWx87O4YWgGgzsmce24eVz5+lweOCOTxVsKKCx1cXLXhln7QFvgyhKpCdE4bEKLGKfVUZSq8uRXq5m4YAttE6IpLHXxyeLt3PDmfH7e41tV8qUfc4mNsHN2rzYADOqQyBs3ZrNtfym3vJXDy1NzOatna87tndogebUFrixR7vYQHWHXOVBUSCkudxMb4eDVa/oTG+kg70AZg56cwlfLdnLbyI5MW5vH2b3akBx3aAGHEV2Smf7AKDbsLSYh2knnlGYN9rrWAq4s0bFlLD+t38tn1RZyqGSMqeEetatrd0PNN9Z2n7ofq7b71PIctT9UrTfWlvdIglS/UtNF3/VqhUQO217L5Wp7BaMG1VTYanqa6ruVu7zYbHBBn7YBX1PySDcMzeDTJTv4/aTl/POKvszflA9AUrNIJi7YyoEyd43rtiY1izxsVZ6GogVcWSIqwg7APf75wJU6Go+XoC963T+9BQ+emcnT36ylWaSDKat307tdPL3bxXPtG/MY1CExpKY+lmNt7ZyI7Oxsk5OT02DPp0KXx2vYuLe41ttra/3V1v6q6ytr7fepbf86HusYG4B17V9b5qM9RfV3bPX3b53fHqrdVr2Vf/j2mh/3eNR075of8pcbj9zv7Bd/wuXxbdz0t3NOKFd9GGO47b8L+WH1bsD3bXFPUTnRTjsTfzWEDkGa27suIrLQGJN95HZtgStL2G1C55TgTPCjGpdpD5zCsL/9CECZy0OU0x7U51u8tYBZuXvp3qY5I7u05PtVuxmUkcij52eRlhgT1Oc+VlrAlVIhrW1CNE674PIYTnt+Oi9d1Z8+7eKDdqBw2to9lLo8PHxOd4Z1bslDZ3cPyvMEgg4jVEqFvHtP7QLA1vxSLnxlFn3/8j0Hy1xBea6rB6WTHBfJ7z5eVnVKfKjSAq6UCnl3je7CxqfOZur9owAoLHXx7rwtQXmu1vFRvH7dALbtL2Xigq1BeY5A0S4UpVRYEBE6tIxlWOckZuXuqzqZJhAWbdnPhFmbcNiE9kmx7DpQCkCqf+76UKUFXCkVVmbl7gMI6AHFO/63iF0HykiNj2LS4u3YBC7u15bzGuiMyuOlBVwpFZZ+3lMUsKXKOqc0Y9eBMnq2jeelq/uRlRof9NEugaAFXCkVForL3YyftbHqetuE6IA99qvX9mfcTxuZMGsj363aTVpiNBPHDiE1gM8RDFrAlVIhz+M1XPTqLNbtLqJPu3gev7DXCbWQp6zezb9nbAAgNsLOwA6JnNQhkSinjae/WcvW/FLm/LyPS4J85ueJ0gKulAppeQfKuOWtHNbtLiI2ws6ndw0/4cd86cdclmwtwGHzHRidunZt1W3tWkSTGBtBv/SEE36eYNMCrpQKSYUlLu6buJipa/cAkBDj5O2bTwrIY//+rG789oOl7CgsZUinJP5zfTY7C8toEesks1Vc2MySqQVcKRVy5m/M5/J/zwHghiHtuSw7LaBrSw7umMR3/zeSf3y7lrfmbOKr5Tt54IxMhnRKD9hzNAQt4EqpkPPwJ8tx2oXxNw5ieJeWQXmO2EgHj56fxYX92vLkV6v53cfLqfAYrhvcPijPFwx6JqZSKmSUVLh54Yd1rNtdxJjurYJWvKvrm5bAe7cNpnNKMz5auC3ozxdI2gJXSlnO6zWMfTuHH1bnAdAvPYGHz+0R9OfdfaCMz5bs4IvlO8nNKwr6fOOBpgVcKWW5309axg+r8+ifnsBvTstskJb3V8t3cu/7i3F5DL3axvOHs7tx/ZCMoD9vIGkBV0pZ6uOF2/ggZxsD2rfgo9uHNNgIkNem/0x6YgzjbhhoySINgXDUPnARiRKR+SKyVERWishj1W67W0TW+rc/HdyoSqnGZm9ROb/9cCkA467PbrDiXebysHrnAU7Pah22xRvq1wIvB0YbY4pExAnMFJGvgWjgAqC3MaZcRFKCGVQp1fjc7y/eT1/amxaxEQF7XGMMxoDXGLz+33Do+uzcvbg8hj7tEgL2nFY4agE3vsXxivxXnf4fA/wa+Jsxpty/X16wQiqlGp9vVuxkmv8knVen5vLyj7l4Dyu8vmJrqhVhr9e3omf1fYzhF/epj/ZJMYxogL72YKpXH7iI2IGFQGfgFWPMPBHpCowQkSeAMuB+Y8yCGu47FhgLkJ4eXoPklVLB0zYhhj5pCbRNiMJpt2ETQQRsItj8v+Wwy74Fn202OWwfariPUG2brfK+h/ax2YSzerYmNjK8DwMe06r0IpIATAbuBt4HfgTuBQYCE4GOpo4H1FXplVLq2NW2Kv0xnchjjCkApgFnAtuAScZnPuAFwvv7iFJKhZH6jEJJ9re8EZFoYAywBvgEGO3f3hWIAPYGK6hSSqnD1acDqA3wlr8f3AZ8YIz5QkQigDdFZAVQAdxQV/eJUkqpwKrPKJRlQL8atlcA1wYjlFJKqaPTyayUUipMaQFXSqkwpQVcKaXClBZwpZQKU8d0Is8JP5nIHmDzEZtbEn7DD8MxM2juhhSOmSE8c4djZji23O2NMclHbmzQAl4TEcmp6QyjUBaOmUFzN6RwzAzhmTscM0NgcmsXilJKhSkt4EopFaZCoYC/bnWA4xCOmUFzN6RwzAzhmTscM0MAclveB66UUur4hEILXCml1HHQAq6UUmHKkgIuIn1EZI6ILBeRz0WkebXbevtvW+m/PcqKjDWpLbeIZIhIqYgs8f+8ZnXW6ur6e/tvTxeRIhG536qMR6rjbz2o2t95qYhcZHXW6urIfZqILPRvXygio63OWqmOzEkiMtX/2njZ6pxHOkodeUhEcv2Lrp9hZc4jiUhfEZnrfw3niMgg//YIERnv//csFZFRR30w3+KfDfsDLABO9l++Gfir/7IDWAb08V9PAuxWZDzG3BnACqvzHWvuard/DHyIb1k8y/Me5W8dAzj8l9sAeZXXQ+Gnjtz9gFT/5Z7Adquz1iNzLDAcuB142eqcx5C7B7AUiAQ6AD+HWB35DjjLf/lsYJr/8p3AeP/lFHzLWNrqeiyrulAygRn+y98Dl/gvnw4sM8YsBTDG7DPGeCzIV5vacoe6WnOLyIXABmBlw8eqU42ZjTElxhi3f3sUvgW2Q0ltuRcbY3b4t68EokQk0oJ8Naktc7ExZia+NW9DUW2v6wuA940x5caYjUAuMMiCfLUxQOW3hXig8nXRA5gCVYvEFwB1nuhjVQFfAZzvv3wZkOa/3BUwIvKtiCwSkQctSVe72nIDdBCRxSIyXURGNHy0OtWYW0Rigd8Bj1mUqy61/q1F5CQRWQksB26vVtBDQV2vkUqXAIuNMeUNlqpu9ckcimrL3RbYWm2/bf5toeI+4B8ishV4BnjIv30pcIGIOESkAzCAo/xfBG1JZhH5AWhdw01/xPd150UReQT4DN+KPpV5huNbJLkEmOJfzHNKsHIe6Thz7wTSjTH7RGQA8ImIZBljDjRIaI4792PA88aYIhFpmKDVHGdmjDHzgCwR6Y5vtaivjTEN1ko83tz++2YBf8f3bbPBnEhmKx1n7ppezA36Te0ouU8F/s8Y87GIXA68gW+pyjeB7kAOvjmjZgN1N05CoD+oKzDff/lKYEK12/4EPGB1xqPlruG2aUC21Rnr8ff+Cdjk/ykA8oG7rM54jH/rqeHwt/ZfbwesA4ZZne1Y/tbAjYRgH3htufG1aB+qdtu3wBCrM1bLU8ihc3AEOFDLfrOBHnU9llWjUFL8v23Aw0DlqI1vgd4iEiMiDuBkYJUVGWtSW27xLfxs91/uCHTB168cEmrLbYwZYYzJMMZkAC8ATxpjQmK0QR1/6w7+1wYi0h5fP+gmi2L+Qh25E4Av8RWWWZYFrEEd78eQVkfuz4ArRSTS3xXRBZhvTcoa7cBX28C3MPx6AH/di/VfPg1wG2PqrH9W9YFfJSLr8K1uvwMYD2CM2Q88h+/o8hJgkTHmS4sy1qTG3MBIYJmILAU+wtcvm29RxprUljuU1ZZ5OLBURJYAk4E7jDGhNJVobbnvAjoDf6o2DDLFqpBHqPX1ISKb8L0nbxSRbSLSw5qINaqtjqwEPsDX+PsGuNOE1mCI24Bn/fXiSWCsf3sKsEhEVuM7NnXd0R5IT6VXSqkwpWdiKqVUmNICrpRSYUoLuFJKhSkt4EopFaa0gCulVJjSAq6UUmFKC7hSSoWp/wf/pGj3/Vp+3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buffMAP = enactedMAP.buffer(0.00001)\n",
    "plotPoly(buffMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df8ba3b4-0266-4ec9-a136-b4870e4591c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tractMAP = tractGeom[0]\n",
    "for t in range(nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if tractPop[t] > 0 :\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "plotPoly(tractMAP)\n",
    "plt.show()\n",
    "replacement0 = tractMAP.difference(buffMAP)\n",
    "plotPoly(replacement0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77b6570b-c45d-43ad-8fcc-120cdd76cef6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "replacement0 = tractMAP.difference(buffMAP)\n",
    "plotPoly(replacement0)\n",
    "POINT1 = Point(-94.6,36.5)\n",
    "POINT2 = Point(-93.2, 36.5)\n",
    "POINT3 = Point(-92.7,38)\n",
    "POINT4 = Point(-94.6, 37.65)\n",
    "healPoly = Polygon([POINT1, POINT2, POINT3, POINT4])\n",
    "plotPoly(healPoly)\n",
    "plt.show()\n",
    "fixedPoly = healPoly.intersection(replacement0)\n",
    "plotPoly(fixedPoly)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "32983215-cb68-493a-a5f6-a84b7886742c",
   "metadata": {},
   "outputs": [],
   "source": [
    "enactedGeom[9] = fixedPoly  #overwrriting the broken FDO alt district 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "561c977d-a901-46a1-8dbc-b54cea5bc503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.592895256894746 1.7104375623696335\n",
      "18.593135486203668\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(enactedMAP.area,fixedPoly.area)\n",
    "enactedMAP = buffMAP.union(fixedPoly)\n",
    "print(enactedMAP.area)\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fa80327a-b031-4ac0-89cc-4668e440970a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotPoly(enactedGeom[0])\n",
    "plotPoly(fixedPoly)\n",
    "plt.show()\n",
    "enactedGeom[0] = fixedPoly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "611f8635-c6be-4e35-be05-6bbda83d23a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n",
      "working on tract 2000\n",
      "working on tract 2500\n",
      "working on tract 3000\n",
      "finished joining DP03 to tract pop data\n"
     ]
    }
   ],
   "source": [
    "#(OPTIONAL) READ IN SOME ACS DEMOGRAPHIC DATA\n",
    "#FOR EXAMPLE: DP03_0041E = Prof/sci/mgmt employed pop, \n",
    "# DP03_0041PE  is the estimated percentage in this field.\n",
    "# contrast with DP03_0033E and 0033PE for agricultural / fishing / hunting / mining\n",
    "infilename = \"OH_ACS2019_DP03.csv\"\n",
    "DP03 = pd.read_csv(\"state_map_files/Ohio_ACS/\"+infilename)\n",
    "DP03AdultPop = DP03['DP03_0001E']\n",
    "DP03WorkerPop = DP03['DP03_0002E']\n",
    "DP03Prof = DP03['DP03_0041E']\n",
    "DP03pctProf = DP03['DP03_0041PE']\n",
    "DP03Ag = DP03['DP03_0033E']\n",
    "DP03pctAg = DP03['DP03_0033PE']\n",
    "DP03GEO_ID = DP03['GEO_ID']\n",
    "# Now, do a crude join of the data\n",
    "adultPop = [0] * nTracts\n",
    "workerPop = [0] * nTracts\n",
    "totProf = [0] * nTracts\n",
    "pctProf = [0.] * nTracts  #fraction of civilian workforce in this tract that's in professional, mgmt, etc.\n",
    "totAg = [0] * nTracts\n",
    "pctAg = [0.] * nTracts   #tract civilian fraction in agriculture, forestry, etc.\n",
    "for t in range(nTracts):\n",
    "    if t%500 == 0 :\n",
    "        print(\"working on tract\",t)\n",
    "    for dT in range(len(DP03Prof)):\n",
    "        if tractGEO_ID[t] in DP03GEO_ID[dT]:\n",
    "            adultPop[t] = DP03AdultPop[dT]\n",
    "            workerPop[t] = DP03WorkerPop[dT]\n",
    "            if DP03Prof[dT] > 0 and DP03pctProf[dT] != \"-\":\n",
    "                totProf[t] = int(DP03Prof[dT])\n",
    "                pctProf[t] = 0.01*float(DP03pctProf[dT])\n",
    "            if DP03Ag[dT] > 0 and DP03pctAg[dT] != \"-\":\n",
    "                totAg[t] = int(DP03Ag[dT])\n",
    "                pctAg[t] = 0.01*float(DP03pctAg[dT])\n",
    "print(\"finished joining DP03 to tract pop data\")  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c43c753-d9ea-453b-ae04-86bbd96ea2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DP03_0001E</th>\n",
       "      <th>DP03_0001M</th>\n",
       "      <th>DP03_0001PE</th>\n",
       "      <th>DP03_0001PM</th>\n",
       "      <th>DP03_0002E</th>\n",
       "      <th>DP03_0002M</th>\n",
       "      <th>DP03_0002PE</th>\n",
       "      <th>DP03_0002PM</th>\n",
       "      <th>DP03_0003E</th>\n",
       "      <th>DP03_0003M</th>\n",
       "      <th>...</th>\n",
       "      <th>DP03_0136E</th>\n",
       "      <th>DP03_0136M</th>\n",
       "      <th>DP03_0136PE</th>\n",
       "      <th>DP03_0136PM</th>\n",
       "      <th>DP03_0137E</th>\n",
       "      <th>DP03_0137M</th>\n",
       "      <th>DP03_0137PE</th>\n",
       "      <th>DP03_0137PM</th>\n",
       "      <th>GEO_ID</th>\n",
       "      <th>NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3275</td>\n",
       "      <td>298</td>\n",
       "      <td>3275</td>\n",
       "      <td>(X)</td>\n",
       "      <td>1593</td>\n",
       "      <td>199</td>\n",
       "      <td>48.6</td>\n",
       "      <td>5</td>\n",
       "      <td>1593</td>\n",
       "      <td>199</td>\n",
       "      <td>...</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>31.1</td>\n",
       "      <td>8.8</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>32.9</td>\n",
       "      <td>10.2</td>\n",
       "      <td>1400000US39001770100</td>\n",
       "      <td>Census Tract 7701, Adams County, Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3994</td>\n",
       "      <td>334</td>\n",
       "      <td>3994</td>\n",
       "      <td>(X)</td>\n",
       "      <td>2004</td>\n",
       "      <td>205</td>\n",
       "      <td>50.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2004</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>14.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>30.7</td>\n",
       "      <td>9</td>\n",
       "      <td>1400000US39001770200</td>\n",
       "      <td>Census Tract 7702, Adams County, Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5594</td>\n",
       "      <td>430</td>\n",
       "      <td>5594</td>\n",
       "      <td>(X)</td>\n",
       "      <td>3164</td>\n",
       "      <td>461</td>\n",
       "      <td>56.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3164</td>\n",
       "      <td>461</td>\n",
       "      <td>...</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>27.3</td>\n",
       "      <td>12</td>\n",
       "      <td>1400000US39001770300</td>\n",
       "      <td>Census Tract 7703, Adams County, Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3339</td>\n",
       "      <td>372</td>\n",
       "      <td>3339</td>\n",
       "      <td>(X)</td>\n",
       "      <td>1703</td>\n",
       "      <td>294</td>\n",
       "      <td>51</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1703</td>\n",
       "      <td>294</td>\n",
       "      <td>...</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>15.6</td>\n",
       "      <td>10</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>41.9</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1400000US39001770400</td>\n",
       "      <td>Census Tract 7704, Adams County, Ohio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2861</td>\n",
       "      <td>290</td>\n",
       "      <td>2861</td>\n",
       "      <td>(X)</td>\n",
       "      <td>1362</td>\n",
       "      <td>172</td>\n",
       "      <td>47.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1362</td>\n",
       "      <td>172</td>\n",
       "      <td>...</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>22.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>(X)</td>\n",
       "      <td>(X)</td>\n",
       "      <td>43.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>1400000US39001770500</td>\n",
       "      <td>Census Tract 7705, Adams County, Ohio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 550 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DP03_0001E  DP03_0001M  DP03_0001PE DP03_0001PM  DP03_0002E  DP03_0002M  \\\n",
       "0        3275         298         3275         (X)        1593         199   \n",
       "1        3994         334         3994         (X)        2004         205   \n",
       "2        5594         430         5594         (X)        3164         461   \n",
       "3        3339         372         3339         (X)        1703         294   \n",
       "4        2861         290         2861         (X)        1362         172   \n",
       "\n",
       "  DP03_0002PE DP03_0002PM  DP03_0003E  DP03_0003M  ... DP03_0136E DP03_0136M  \\\n",
       "0        48.6           5        1593         199  ...        (X)        (X)   \n",
       "1        50.2         3.8        2004         205  ...        (X)        (X)   \n",
       "2        56.6         6.2        3164         461  ...        (X)        (X)   \n",
       "3          51         5.1        1703         294  ...        (X)        (X)   \n",
       "4        47.6         6.3        1362         172  ...        (X)        (X)   \n",
       "\n",
       "   DP03_0136PE  DP03_0136PM DP03_0137E DP03_0137M  DP03_0137PE  DP03_0137PM  \\\n",
       "0         31.1          8.8        (X)        (X)         32.9         10.2   \n",
       "1         14.1          5.4        (X)        (X)         30.7            9   \n",
       "2          8.4          6.8        (X)        (X)         27.3           12   \n",
       "3         15.6           10        (X)        (X)         41.9         14.3   \n",
       "4         22.4          9.4        (X)        (X)         43.7         11.7   \n",
       "\n",
       "                 GEO_ID                                   NAME  \n",
       "0  1400000US39001770100  Census Tract 7701, Adams County, Ohio  \n",
       "1  1400000US39001770200  Census Tract 7702, Adams County, Ohio  \n",
       "2  1400000US39001770300  Census Tract 7703, Adams County, Ohio  \n",
       "3  1400000US39001770400  Census Tract 7704, Adams County, Ohio  \n",
       "4  1400000US39001770500  Census Tract 7705, Adams County, Ohio  \n",
       "\n",
       "[5 rows x 550 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DP03.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6645af44-a057-46e2-a425-74de8a03de5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n",
      "working on tract 2700\n",
      "working on tract 3000\n",
      "finished joining DP02 to tract pop data\n"
     ]
    }
   ],
   "source": [
    "# ... and now the college educaction level.  From a different file\n",
    "infilename = \"OH_ACS2019_DP02.csv\"\n",
    "DP02 = pd.read_csv(\"state_map_files/Ohio_ACS/\"+infilename)\n",
    "DP02totBach = DP02['DP02_0068E']\n",
    "DP02pctBach = DP02['DP02_0068PE']  #pct Bachelor's or higher\n",
    "DP02GEO_ID = DP02['GEO_ID']\n",
    "totBach = [0]*nTracts\n",
    "pctBach = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    for dT in range(len(DP02pctBach)):\n",
    "        if tractGEO_ID[t] in DP02GEO_ID[dT]:\n",
    "            if DP02pctBach[dT] != \"-\" :\n",
    "                totBach[t] = int(DP02totBach[dT])\n",
    "                pctBach[t] = 0.01*float(DP02pctBach[dT])  #percentage\n",
    "print(\"finished joining DP02 to tract pop data\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04302b0a-dc16-46ce-bc0a-76c2e2844219",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a plot of total Professional and agricultural workers vs. tract pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here's one with pctage by tract Pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here's where in the state we see high Ag (red) and high Prof (blue) pctages\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here's where in the state we see high pct Bachelor or higher\n"
     ]
    },
    {
     "data": {
      "image/png": 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rzPizuKdMgZQUaGryv62UgcU7ll6TO2LJWS2C0kF5HknVNB3n3nvh4Yfdv998E0aNgnXr2n4LMpOUBIMHwybTwKSBDJVYp7OkK/GEXggi6GaPW3xZ3Mb8P/4R3n0Xvv4a9uxpva3FEli8Y+01uatacrGC4Y4BGDMGHn3Uc7mUsHat722F8BTASZOge3d1j27Zoub5uifjLZQyqgOPxBsWAU5t0QfE2+LOy3Nb+DaberDMDWDeBHpFhtgX13gTgnilvFxd42bX8LJWKzidgbezWJT4md04FgtcfLGafusttR+LBS68EK64wm2wQOy0EcUCCSf0Qggt9EHgbXGbLXynM/ArZLy/IsdSY3EiYq5Ey8o8jYZA/neLBWbNgoICeO4593whlFvRsNrNhsoVV6gIHOP3ddfFThtRLJBwQm8RwVkLmtYWd3Ky8s0Hun7mhy1eWbzYHZbnTwi0xR8a3pXonXcGv60h8vPmwfr1StwtFuWPnz5diT+4DRXDHfT1157CDu43B6s1/u/XjhK00AshrMBq4ICUcqYQ4irgfmAYMEFKudrPdruBOsAB2KWU4zta6Lawaos+JIwH5+67VeiaPyZMCM5tE8uUl6vON8Zt4ksItMUfHEZlmJen3vIMC94wGBoaPBtdA+F0qobZd99V/48h1HfdBU88of4PI+IL1HRzs1rH5lKz5GTVDmD4vBM4cWnQtMeivwvYDGS7fm8ALgcWBrHt+VLKw+0sW0gI3RgbMqWlMHZs20I/dmz8C15ZmTs+Wwi44YbW5xRL4aGh4t0AaggxtBZnX+dWXq5E+ttv4fTTlUVt3sfDD8OSJe43QCEgNRUuv7xjb9XGW5YxLYRqqPUVPGDMA7j5Zigudlc2drva3m6Pz/8vnAQl9EKIPsAlwG+BnwJIKTe7lnVa4ULBYtGNsR1hzpzWPRENUlLU8njHuyHa1znFUnhoKHg3gIJ6Nmw2JZzNze43GpsNvv99qK5Wvu6SEiXib7zh3nbTJvfvlBQlnt6+dimVO+yvfw293EYDrBEQ4HCo6fR0T4vdl5/euxNfPP9/4SZYi34BMA8IJe2gBD4QQkhgoZRyka+VhBBzgbkAxcXFIRxGoV03HUcI9bFa4aabPK3BRLCKggn9jLXwUDPBJPfau7d1Ze10uueZHxG7Hf78ZzX9wQeBj99WHwvvUMj2MnIknH22u/I18ti89Za6H2++2VPQFyyAV19VFZT5WpSW+l/WFQko9EKImcAhKeUaIcSUEI4xUUpZIYToCSwTQmyRUn7ivZKrAlgEMH78+JBvFYsQOLTvJmTKypQVZXQtLy6GuXOjXarwE0zoZyyGh7bVdmBeZviszWJvWPSGS6MzuPpqeO015ZsPhXXrYPNmNT1njrr/7HZ3JFhxsef5GpE2K1aoN5FglnVFAg/OCROBWa5G1b8BFwghXgn2AFLKCtf3IeB1YEII5Qwa1TO2M48Qv5SXw0MPqW9/GC4Lq1W/8poJ5tpFYr/+Orp5L3M4VNKvW25Rn4UL4Te/UQ2aPXuG9xzMZGWpyueWW1TF0haGm8aMlOqNYeFCVWnV1Lj9/U6nalcwCPZaeC/rigS06KWU84H5AC6L/h4p5bXB7FwIkQFYpJR1rulpwK9DLm0QWATaovdBsFEk7XVZdIUQxEWL4I47lGikpPi+dqFch1Aie9pqO5gyRVXQhu88Oxtyc92Nrnl5ysoN1dr2pmdPOHTIc95zzylX3zPPKIv84Yfh88+hqsq9jsWilhttAeYGXQND8F97ze0Oslg8+28EuhbaR29CShn0B5gCvO2a/i6wH2gCDgLvu+YXAUtd0wOBda7PRuAXwRxn3LhxMlS+89jH8uaXvwx5+0TlwQeltFqVQ8ZqVb/bYuFCKadNU9/++OwzKdPS1P7S0tTvROOzz6S02QxHlpQWS+trt3ChlElJalmw1+Gzz9T1tViC/0/M2z74YOvjfPaZKofb8SalEO5yG2U0L2/rY9wv/j5CuPdv/iQlqWtiLuPChVJOmCDlZZe557VVXu/fQkiZnNz6nNu6T/1dp1jl1ldWywv/UBby9sBq6UdT29VhSkpZBpS5pl9HuWK816kAZrimdwKj2nOMjmLRjbE+CdbCMULqjAiLDz6AHTvg979vvW4ihCAGoqzM09r0jrkvL4fbb3eHazY1Bb4OhiVvxJoHkzvIjL+2A3PYqIHxKBjnYLEEH/oYqAerv8fMblcZKMH9plJSApdd5n7jKS+H++/3X17v30aYpZlAfvhYbGOJFgnXM7ajrf6JSjAuGUOAvF/tH30UBg1qHXnTFV6Pzdk8LRZ48knPaxeoIvCFUUGa87Tcf3/HRclw3fjK4W6xqPNYsEB1RjKHToYbIywS1HV7+GF4+211vkYZ7r5bLWvrWTV6thrreMfDx6qhEYvuzIQUeo1vAlk4xoPjjZTKapXS05ccyyGI4SLQOQaqCHzhXUGGQ+SNst50k2rINHzaRrIvcyU9d65KE/zSSyoK5+DBzksb4nR6+uCbmlTIY6BUG1YrPP20Sm1gHkKwPX74aAhurPaoTjihBxW4r2k/5gcHPFMEGMnOvC2nrvB63NY5hlLZdbSC9CVgxrwxY1Tv1LYqkfJydzqB5GS45x74wx+CG+wjlDdmb0FvbAyuYikpUZXSmDH+Y+X9XcdIC665/0IsvmUknNAL9MAjoeL94IC7q7w5M2Aiumg6QiiVXXu3MeeUMf8XRs4Xs6gtWNB2Bzdvl0durvJxX3217/EHvOmIe9TpVMcKZj0jJDIUP3wk3TrmSsVmc496Fcqz0lnKlXhCL7RF3xG8HxxjuqQksV00sYxZSCyW1m9X4ClqR47A/Pn+9+fL5VFaqnLZPPts22Xx1Sg6YYIaDvDNN90VQP/+qtLwVSEEU0lYLKpSu/9+t5unPYIdyfYjc6UiJYwfr3JCBRpX2RvR5oCUHSPxhB7dGNsZdAUXTShEwg/sLSRGRyN/OV8CiZr5zS0vz11ZtJXnyMDIQ2OUxXiDANXIa6RH2L9fLbPblYUrhNqv0+lOr+F0ul04Vqs6L/Ox77zT3Ss2lMikSLUf5eWp8hnZNlevVimWYykvVMIJPUJoi14TESLlB/a2Tn25ZkJpJ4DW5X/ySXj8cc9xWM2ceaZ7yD+rVQ07aezr+uvdDcFOp2oYNrJJrl/vbtC3WuGpp1RDq7G+lJ7jv5oHwBECBg6En/2sfdc3EsaJEeJpbt9wOoMLs40kCSf0OuhGEyki5QcONglbe4/tXf7Fi1V+98ZG3+vbbMolsWaN2zI391QdM8Yzbn/MGHeepLIyz25VR460Xt/7TcJIrud0ws6dSlBjLWeNd6isgXe6hmiTcEIP6MZYTUSIpB+4M6zTvDz3CE7JyWqeOX2x0ehquGuefFIJrTHYh/c5f/21/3QFZveGzaaiU/bu9WzY3bbNs3znnqsiiJYti00rGTzvAeP8vM8/FuLqE07odRy9JlLEcz8Cw+VgjOC0YIFbxL3TEFutSuQN69xX+t/ycpVO2BySu3evO1mb4d4wLPTnngscvVNervZvtvpjyUqG1u0d3tFpsRJXn3hCj26M1USOeG2kNrscDBeMkcP9tts8fc4Oh7LWwX/aASO9NXiK+csvq4G6zR2kgonXB+XK+cQroblRjljCfA94R6c99JDbPdbYqNxj0bhfgklTHFcIIZC6OVajaRNf6ajLy1XUja+Ro156ye2C8JX+13DNGB+nU63T0ADvvRe+nrfejcSdlT66PZjLUFqqQlvNaUKMkbGkhOefj05ZE9Ki1yQeseDnTCR8dY6bOtV/Q2xLi3tdIzTSyOtjjjwxGlDNb9W7d4ev3ObyxYJbJFAZjP4JRm4huz06Vn3CCT1o101HiEVBjYUHOhExuxwMF4O/Z8ecrM1oBzO+y8rcrplwtZEZvUu93y5uvNE9bT5utBpq24q8Mp6lWCDhhF5nrwyd9ghqJCuExYuVJSdlbOUPSSTM0SM2G5x1lqd//Cc/cfucjaEIjWySeXlu10w4nz0jqdmmTer/v/FGz2EtzceNVkOtv8grcypqo4OYwwFJSdHpSJV4Qq+dNyFjtk6amlT3c+/MhxBZC7u8XPmHzdEcOtdO+PF25ZSVwcqV7vjw3Fy1nneF4CtM0kxbhpcQMHu2Z49aAyPW/pln/Jf5yBF3e4B3OGek8Bd5ZTZOzEQrKjBooRdCWIHVwAEp5UwhxFXA/cAwYIKUcrWf7S4GHgeswPNSyt91uNQB0I2xoWE0HBld05ctUwOPGLnMDUGPZMKosjJ3fnUh4IYbtDUfLrzfyrwjiFJSWluqhrAtXqzCKZ97zh0/bmAeztBmU5Z4drY6VmoqfPGFO+3wvHnqU1amxod97DH3kI2BKnQjRXS0k+2Z8+MbmENNzTQ3q/YMI21EpN6K22PR3wVsBrJdvzcAlwML/W3gqhyeAi5CDTv4pRBiiZTSTwfrMNAFXTfhdKMYHT6MaWidUCqSHYW8jxVL+UPimWAaEb0tVfN9VlzszkNj+OaNjkLjxsGXX7pTIRQXq0gUY/trrmn9lmjsv7ZW/Q4mIVis9GPwvpbXXdd2COmqVTB5sju3j9FPgYzOK2NQQi+E6ANcAvwW+CmAlHKza1lbm04AtruGFEQI8TdgNtBpQi/oWtkrw+lGMcdCe2N2mUTyAYuVhznRCOatzGzhe99nd97p6Zc3p+a98UaV2ybYjkPl5eoNwTzAyJgxwf3nsdCPwftagjvxmz9aWtyVo9Op8gBd/ofOK2OwFv0CYB6Q1c799wb2mX7vB87ytaIQYi4wF6C4uLidhzHvp2tZ9OF0o5itZ/BMKuXtMonkAxYLD3Oi0d63Mu/7bO3a1s/azTe7LXHvjkO33uq7Qd2oAMz+7KYm1WnL6VRlW748tv9/f2+dRsI2XxgZSI3KwOmE6mqwdOucMgYUeiHETOCQlHKNEGJKO/fvy9z3eepSykXAIoDx48d3TKq7kNCH043SVndu7TJJLNr7puR9n11xBXz0kbv9RErlojG7YvylR7DZPAe28TV2rHnM2Wj1Jg0Wf9fSSCdhdm2Be5hEUJa8MZZufj4cbukc8QrGop8IzBJCzABSgWwhxCtSymuD2HY/0Nf0uw9Q0f5iBo9AIOmkATBjkHC7Ntrqzq1JLNrzpuTvPrvjjsCNp97pEa6/3rPnqBE5Yyzv2ROqqtzbm6djFe9r6W00HTni/jZfP/Mz9souOFzZOeULKPRSyvnAfACXRX9PkCIP8CUwWAgxADgA/AD4YUglbQddLcSys1wb2mWiMeN9P8ydG9gYKC9X4ZdmH7757bC0VOWmNyqMpCQ4dMhzHwUFHSv3okXuJGzmOPzOJpjnx7zOK7s6rywhx9ELIb4LPAHkA+8IIdZKKb8jhChChVHOkFLahRB3AO+jwitflFJuDEvJNT6JxZ6tmsSlLTHzHkvV7MM3Y64w9u5Vvm0DIVRo5kMPhXZPL1oEP/6xmv7gA/fxuhrtEnopZRlQ5pp+HXjdxzoVwAzT76XA0o4Usj105Rh6nSpAEy68DQZ/BkRbhoW5ARc8ffj+tqur8/TXSwkPP6wEPzW1/ff0q6+2/q2FPlHooOcmXq3iSHZk0nScYO6zzrgXA+3T22BYsMDdMG+1qggsw/3SlmHhL1Bg0SLlqjHGk/3pT9Xwhd69Y81IGVo+myuucFvyxu+uSGIKfQeIZ6s4kh2ZNB3D130Gra3ocN+L3jlYLr1U9Uw179fbYHj1VXf0iMOhXCtGnvm2DAt/na5uv90drWO3w6OPBpfG2GJp/z1tWO/R8NHHElrovYhnq1h3LoofvO8zY7xWs6h3xr1ozvgIKn3uu+96xqp7GwyjR3taxUYsfFWV51CEvkTY24dfVtZa1IMReWNA8VDOf+7crivwBgkn9B3tLBXvVrGOlIkPvO8zaC3qnXEv+srw6F2J+Epw5h0CabWqCsI8FGEw952RT8nolAee+/aFEKoht6uLdUdIOKGHjrnotVWsaQuzfxtCv0+87zNoPeh2uO9FY4AQb2PIVyXiL8GZzabi4EElNDMPRRgMpaXKx2/0GhUCZs2CpUvd4m+xqOMI4U6JoDvsdYyEFPqOEg2r2BAQX50qNLGB2WdutXoKkS8feyC87zNfoh7Oe9FwBRnJx8aPh7FjAycQ8+dr966YgmXOHM9tzRkszfe/UWb9LHSchBN6SfwNJ+id7yPUUDJN52L2mZsTepl97E1NSkSfeqr9robONjC8XUHBult8la0jbxv+tvW1D33/h4eEE3qIXnL/UPEepCDUUDJN52IWSm+LHtyNnE6nCh8sKYmt/68z02VEcltN+0lIoY8XjPSsL7zQ2m8aSiiZpnPxFsr1691heyUl8PzzbkvfGGYv1sRMC2zXJPGEPk58N77Ss4KyEm02NRCBfiBjD0MojYbN5mZYsUJVAD/9qerFCeo/jcYYphqNLxJP6ImPpGbmhjFQAp+crCIaghldRxNdfMW45+ZGfwxTjcYXCSn08YC3v9foVq4FPj7wF+MeC2OYauKPyuMNrNl9jIwUa6fsP+GEXsaJ70bH68c3/kIOr7tOLdeVtsabAzUNfPJtNV/uOkr1iSbOHpjHySY7y7dWs7lSDZY78bTO8fclnNBD/ETd6Iax+MZ7FCVzXhrdwUdj0GR38EzZDp5evoNmh5Memcl0S0/mkfe3YrMIxvfvxvzpQykdlMfQguxOKUNCCr0mcYnVzKLxnCNJ03nYHU6+t/Bz1u2rYdaoIv7f1MEMys8AYNuhExTkpJKdmtTp5Ug4oe9KA4N3NWI5s2i850iKBl2hN3hDi4MNB46TbLPws+8MoW/39FPLTu+VFbFyBC30QggrsBo4IKWcKYToDvwd6A/sBr4npTzmY7vdQB3gAOxSyvEdL3agsnb2ETTRIJatZt3m0j7M6ZKNKKWUlNiqvMNBVmoST18zlnv+uY5L/riCBy8vYebIooiXw9KOde8CNpt+/xz4UEo5GPjQ9dsf50spR0dC5DWJi2E1W62xaTWXlsL8+YklVJ2FUWkbHcycTtWnZPHiqBarU/jO8ALeufM8BuRncsdfvubT7YcjXoaghF4I0Qe4BHjeNHs28LJr+mXgsrCWLES05yZxMazmBx5IPMuvq2FU2haTAkkJL72krP1Eozgvnb/PPRuAa57/AhlhH3OwFv0CYB5gzhrdS0pZCeD67ulnWwl8IIRYI4Twm+ZJCDFXCLFaCLG6uro6yGL52VcchFdqQkNbzYmBUWn/5jdw2WVud6uROiIRSU2yMun0fADm/mkNTXZHxI4dUOiFEDOBQ1LKNSEeY6KUciwwHbhdCDHJ10pSykVSyvFSyvH5+fkhHio6lJerUeoT0RLRRIaueA8Zlfa8eSpba6y65MLJ//3oTG4+bwDLNh3kH1/ui9hxg2mMnQjMEkLMAFKBbCHEK8BBIUShlLJSCFEIHPK1sZSywvV9SAjxOjAB+CQ8xfd5vM7atU9iORJEExu0dzDurnYPdaWGbItFYHG9vvTulha54wZaQUo5X0rZR0rZH/gB8JGU8lpgCeDqB8h1wJve2wohMoQQWcY0MA3YEKayxwRGimFzJIhGY2CI+H33qW9fFruvaKKuRldxye2sPsHCT3Zy1bg+nD/En7c7/LQn6sab3wEXCSG2ARe5fiOEKBJCLHWt0wtYKYRYB6wC3pFSvteRAgdDpMIry8vhxRfdsfs2W2K/dsYS8eLqCEbEYz2aSNMxzPfqsk0HAbh0VBEignHg7eowJaUsA8pc00eAqT7WqQBmuKZ3AqM6WshYpaxMPcCgKpfrr48/iyRWe5q2RTy5OryT1+3dq8ofrtGaNLHNokVqEBqHA/Iu3Ej6mN0AFJs6TkWCjlj0MUkkPfRmSyw1Nf7ymwTjVogWbVns8eTqMET85puVMfDcc76vdVdxXXQlysvh9tuhpUX1Ezi5q8epZYZlHykSTugjSbzHdceqYLZVAZWXK6vYYlHCabXGvqujtBSKi1XoYKxda03nUVbm7hAG0LK7F4+cr4IOW8wLIkDC5bqJNOHKQBkNF0qs5mfxl+qgvFyVsbnZvW4oQVb6WmsiwZQpKq2DMWD8k0/C+ROTSf/Uymfbj3Dr5EER89MnnNA7nTKijRzhIFo+53D6hsMpnv5EcfFiT5EH9Vq8eHHwx0yEa62JD3z/5ymcP6Qn76yv5Fh9C90zkiNSloQT+ia7k1RbfHmkopmsKxxvJMGKZ7CVgT9RrKryvb6/+b6I92utiS+8//MV26p5Z30ll40uipjIQ4IKfUpS5wzH1VnE+2t9MOLZXkvalygWFPhed+nS1pEsvjD8+1bX7WFc63iMPNLEH/XNdl7+bA9JVsHvrxwZ0WMnntC3OEiJM4s+3l/rg6mowmFJz5mjkl41N6uGWKM9y8iP0tb+zBWNzaaiYIwoqXgJ1dTEJw6n5NEPtvLK53uoa7Tzw7OKSbFF1hhNPKG3O0lNii+hh/h+rQ+mogpUGQRjVZeWwvLlar2aGnj4YTXf6VSDV7SFuaIBFQVTWqpCOGM1x70mMXh6+XaeKdvBJSWF/Ghif8b36xbxMiSk0Ee6ttQErqjaqgza49YxjvPQQyqSwRi04siRtsvnr6KJd7eZJrapbWzhmY93MH1EAU/+cEzUAkUSUOhjy3Wj/b9u/FUGgdw65mtorJ+XB0lJav2kJPX7oYfcw9J5D09XWgoLFsCrr8IVV7j372++RtNRGpod3L9kI/XNDm6dErlQSl8klNA7nJIWh4wZiz6euupHE2+r2hBtQ9iNIeeM58TpVH52p1PF0dvtqpu53e4ZVy+EqgRuuAGys+Gxx1RlsmIFlJS4Y/Pvvlsd2zxfo+kITXYH177wBWv2HOO2KYMY2Sc3quVJKKE3EvmnxIiPPpbHOI0lzG6dvDy38CYnw3XXuccVNdPS4p52ONy+dzNSqv08+6zn/KYm93+h/yNNZ/Dfb2xkzZ5jPHH1GC4dFfkxYr2JDUUME00tSg1iJY5eZyUMHiPXy5EjnsIL4c9GKqW78TYvT/n4LZbI/EfRyLoZL5k+E4Wq4438ffU+bjp3QEyIPCScRa+EPlbi6IOJRtE+fE+83Thz5qgOUW+8Eb5jSAl33qmm775bVSoWi/LVd+Z/EA1XnnYfhk6oz6bFZZjkZaZ0RrFCIsGE3uW6iRGLHtqORukqD2EwIyyZl3tXjvPmqU5RLS2e8fMdobkZHnnE7RYSInDkTqgY57d3b+TdRNo1FRq+nk0ILPwfbj7I/W9tBKAwJzUiZQ2GhBL6RpfrJlYaYwPRFR7CQJWZv+Xe+dqfeEJFxuTnw5//HJ6y7dihrPvOdNt4d9Ty7pXb2ejw0dDwfjYXL4aXX27bKDvRZOfWP39FbloSz1wzlotH+OnKHQWCFnohhBVYDRyQUs4UQnQH/g70B3YD35NSHvOx3cXA44AVeF5K+bswlNsnsWjRt0VXeAgDVWbBpk+4805l0VvC+NcaIj9+PIwdG779mvHuqHXzzaqzVqRcdfHe6zpaeD+bEPg+TbIK0pOtjO6by/SSwkgXuU3aY9HfBWwGsl2/fw58KKX8nRDi567f95o3cFUOT6GGGtwPfCmEWCKl3NThkvvA8NGnxoiP3heB3BSJRqDKrK3l5eXKkvrkE3fDrK/omo4gBKxbB2vWKIst3O4zX20Okf6f47nXdbTwfjbB06L3ZZQlWy10z0im2RHZXPPBEJTQCyH6AJcAvwV+6po9G5jimn4ZNcTgvV6bTgC2u4YURAjxN9d2nSP0husmRsIrvQnGTRHOYwVTgXR2Y3Cgyszfcl+559tLSgpceWXbrh4p3SMAtdd9FmzahkSvzBMV72cz0P/41d5j7Kw+yY/O6R+hEgZPsBb9AmAekGWa10tKWQkgpawUQvga0rw3sM/0ez9wlq8DCCHmAnMBiouLgyyWJ80ucy/JGptC729Ep3CLQFt+ce9eppFoDA4mPYL38rIyz1j5UBg4ED7+uO11pHT7zS2WwDlzDEJJ2xCv6MgwRaD/cf+xBgDGFkc+l00gAgq9EGImcEhKuUYIMaWd+/cVAe1zTCAp5SJgEcD48eNDGvrVeGOyWXwdNvr46gFq9Pq0WtUINHPndvw4vhqSFi9WYYpLl6r5RmekjjYGd5YITJniTnEAavqSS+Cdd4KvADZvDryOlHDBBbBsmdqvEXZpTp/gi67QkA5dJzKsozS2OPhm/3EADtY2MqJ3TpRL5EkwFv1EYJYQYgaQCmQLIV4BDgohCl3WfCFwyMe2+4G+pt99gIqOFtofDlfcnSVGR5jyfo0vK3OH9zmdaiBhc9d8s4C2R1CnTFEVhxE2+MILrcXRENCONAZ3pggYvVYXL1a/Dd/2rbe27unaHnJzVeZLM8uWuVMnNDfDLbeo65aS4v+cukJDOnSdCq2j3Pjyl3y6/Qjj+3VjfP/u0S5OKwIKvZRyPjAfwGXR3yOlvFYI8QhwHfA71/ebPjb/EhgshBgAHAB+APwwLCX3gWHRW2PUoofWr3+GIIP6Ntw5ZgFdsMAzLUAwgmrUdUYuGO9lRsPgnDmhW+SdLQK+XpXnzIHnn299TsFisbjH8QR1/b0beKVUH3OqBF9l6wq+965SoYVCQ7ODyuMN5KQlsbP6JBMGdOcfP47NG6EjcfS/A/4hhLgR2AtcBSCEKEKFUc6QUtqFEHcA76PCK1+UUm7saKH94XCZZbEq9N5WeWmpctfcfrsS+ZQUt6VvFtBXX22foJaVuRN8GT5oQ8ySkuDGGz2jP0IVqWiJwMyZ8NZb6txSUlRF+PXX8MorcOJE29sWFKiKwnhTGDNGXX9fFYfV2vY5xbvvPRi6SoXWXhqaHVzwhzIqjzeemnewtpG6xhayUpOiWDLftEvopZRlqOgapJRHgKk+1qkAZph+LwWWdqSQwWK4bqIp9P5cLuZkXVaryqg4Z47yyZeUeDaQ7t2rOteAEtArrlCZFYMVVG8BNoQQwhveF2kRMLuKzNfQOO6YMfDjH7vXnzdPfRsDlADcdVdrgf76a1i40O2+EcLdZqKFrWtUaO2lvtnOoTr1WvjjyQNZtesog/IzyUiOzT6osVmqEDnluomQj96XqPtyuRhuAsPCdjiUsLz0Elx/vRKr+fNh0SK3dW+zwaWXKgu0pMR/CKIvkY2kAEdSBLw7HxmjRBkYDdlGbnnj96BBreeZmTPHHSNts8H06e7rrtH4Ii8zhf+ZNZxfvrEBixC8ftvEaBepbaSUMfcZN26cDIW/r9or+937ttx/rD6k7dvDZ59JmZYmpdWqvj/7TMoHH1S/QX1PmyalxWLIu++PEGr7hQulTEryXGa1eu4/0PGDLfeDDwa/fixhnLPFoq7VwoWh78f7GhjzFi4M7bpqug4NzXb5wFsbZcmv3pP97n1bPrV8W7SLJKWUElgt/WhqbAach4jd6fLRR8Ci99UQ6Z2W+Ior3DHa/jBypr/6autGQSPPemMj3H+/Z5pZfzH5bWG8cdx3n/qOt7S1xmhQFos677vvbv85+LsG/tIkB3NdNV2LF1bu4vmVuyjOS+dvc8/m1smDol2kgCSW68blZA1nPhR/+GqI9Ocy8dfYZ7G4I2AMP3xDQ+v1pFQhgCtWKKEzhsrz1RBqbhPwjgVPhFC5I0fU9QilJyv4vwbm66ajTDRtMXVYTx55fyvXntWPswcG2cMuyiSW0Luc9LYIKL0/Uff2WRuNrYsXtw4LvPpqqK52+45LSpSVumpV6+MZ4X633qp+G9EmZjE3rFUjNt8IJTTCMRMhVK6j5+Bre++2lTvvhLVrQx9DVvckTUycTsmyzQd5/N/bACjunh7lEgVPYgm9K2oiUo2xwTZEmtczojuEgL/+VQny8uXujlI33ugp9EK4syya87A3NSmRnz/fPc+wVs1x+WarNRFC5Tp6Dr62f+ght5Xf1AT/+7/qmocyhqzuSZqYHKxt5PKnP+NATQN9u6fxu8tLKB0UH9Y8JNhQgk5n5Fw3oTBnDqSmKr+9eQCNlha4/HJlrX/9tbv8QsBFF6nKYfx4z30JocIwzT5qw1o1tveVZ93wRUP8Di9nnEOoAuq9vbltxejAFqqPPpS2E03s8/G31RyoaWBwz0zeu2sSP5hQjIjRHvi+SCiL3miMjYTrJhTM1uSf/uSZi6WqSnXtF0KF+AmhhLqmRg2QsXate11jjNPnnvMM0TTv35eP3kBbnZ54XzdzL+RwuIY08c+sUUW8800lH39bzbm//4gV915AZkr8yGf8lDQInEE2xkbTh2q4UGpqfCfdMlIW9OsHu3crN86qVZ4DZA8dClu3uqNyFi70zKUe6JwSoVE23Jivm7kDWzhcQ5r4JzXJyv9dfyZPfrSdPyz7lp/9cx3PXDsu2sUKmoQSekcQFn2sWLPffut/mZRK5L3nGWzbpqx+p9Mddd8ewdZWZ9t0tBOY7kmaeDidkl+8sZ6/fbkPcHsP4oWEEnrj4reVASFa1qz3W8QXX7Rv+/79Yc8ed2jhjTeq+S++6E473JZgd7WRrTSacLJ2fw1/XbWPswZ058eTB3LuafnRLlK7SCihdzolFkGbjSTRsGZ9he9VVga/fVKSajw0+44Nn3ww2ScjObJVrKFDHTXhwFCUqycUc8HQXlEtSygklNA7pAzYEBtOazZYEVm8WPVuNWLhn38++GNMmKDi5UtLffuOtU/eP7HiptPEJy0OJ06Xprz82W6SbRbOOS1+QirNJJbQO2VQoZXhsGaDFZHychUZY/jYnU44ejS4Y1itbpHvSLm7qk++q1ZwmtCRUvLh5kM8+/EO1u6rwe7yEjgl3DBxAD2zUqNdxJBIOKGPVGepYEXEyA3fXoSAp58OjzB1VZ98V63gNO1HSsmyTQf540fb2HCgln556dw8aSAZyVYaW5wM7pXJzJFF0S5myCSe0EcoF32wImJez2LxHNLP6PXqi9mzwzN+rEFX8cmb6aoVnCZ4pJR8uv0Ij7y/hXX7j9MvL52HrxzJ5WN6Y7PGZn+cUAhmcPBU4BMgxbX+v6SUvxJCjAKeBTKB3cA1UspaH9vvBuoAB2CXUo73XidcRFLo2xIRfxEue/e2HuDCSG1guJycTtX4agyaoekYXbGC0wTHZzsO88Dbm9lcWUvPrBQeuXIk300wgTcIxqJvAi6QUp4QQiQBK4UQ7wJPoMaP/VgIcQPwM+A+P/s4X0p5ODxF9o9DuoW+I4NrB4u3iJSXq9GMvIe5M3qoQuvEZuPGwdixKnoGwltGHXGi0bRmU0Utv3tvC598W01x93QevmIks0YXkZoUIKd4HBPM4OASMEbiTHJ9JDAEZekDLEONC+tP6COC02XRm7M4Wq3wk5/AE090bvRFeTmcf757NClQkTa3365E3zjuU0/BHXco377TCatXw/r17nDJcJVLR5xoNJ4cr2/hoXc38/fV+8hJS+Ln04cyp7Qf6TE6/F84CeodRQhhFUKsBQ4By6SUXwAbgFmuVa4C+vrZXAIfCCHWCCHC6HVujd3VGFtW5k7V29ICjzyifndmoimjcdaMkbjMfNy5c+Hjj+HCC91um84ok06upYlVyssjn1Bv9e6jXPTYx/xzzX5uOncAH99zPrdMHtQlRB6CbIyVUjqA0UKIXOB1IcQI4Abgj0KI/waWAM1+Np8opawQQvQElgkhtkgpP/FeyVUJzAUoLi5u/5ng6jBlEUyZ5Jkbxkjza4z8FK7oC7NrZMoUlZbAaGy1WuE//9PzTcJ83IEDlS/ebu+ciBBzI7DV6s50qa16TTSJxpvma1/t5+evrqcoN5UXrptISZ+czj1gDNKu6kxKWSOEKAMullI+CkwDEEKcDlziZ5sK1/chIcTrwATcLh/zeouARQDjx48PKZFEk8NJis1CaakaWPuNN9zLLr1UdT4K1IM0UOZH87reA4EL4W5gffppZb1fdpn/AcStVrj5Zrd//qGH/B+zvf52oxF48WIVx//cc56JzzSaaOD9prl4sfqA230ZDg7WNnKwtpElayt4fuUuSgfm8cy1Y8lNTw7PAeKMYKJu8oEWl8inARcCvxdC9HSJtwX4JSoCx3vbDMAipaxzTU8Dfh3eU3DT1OIgxaYaVObNg3ffdQvxvHlt30SBRmfyxvuGNcZ8NSJqjhxR395+d7NbSUowXl7asnJCtYJKS91x/LrTkCYW8H7TfOEF91vwiy927P6UUvKP1ft4pmwHu4/Un5r/gzP78uvZI0i2JV40TbAEY9EXAi8LIawon/4/pJRvCyHuEkLc7lrnNeAlACFEEfC8lHIG0Avl6jGO9Rcp5XvhPgmDJruTlCT1Z5aWqpGbgrWCzQIMgcck9Y6jHz0aPvrIbdXn+ekpnZfneYy8vMCdrzrSw1N3GtLEEt7hxs+93Ezm8EpsOfXYj2Xw7ke9KC1Nafd+6xpb+Nk/v+G9jVWMLc5lTml/+nZPp3duGsMKs+JqkJDOIJiom2+AMT7mPw487mN+BTDDNb0TGNXxYgZHU4ty3Ri0J4qlpsZzqD7v0ZnMrhNQ0wsWqBGhqqrg8cfdFr3DoRKQ+RqG7sgRz+EBDRdRW2LcEbHWnYYSF1/uvHgIqS0thXFnOvnlKzvonbEdYXMiHQJhlfytfiNDvjiDa87q53d7h1Pyzf4atlbVcay+hZy0JN7dUMmKbYf5rxlDuencgVgi1J8mXkioJme700m6LbhT8hbuxx7zXH7hhXD//a396kakjLmTk8PhuW1b+eHz8jzz3uTlBRbjcIyTGqsPfbiIB4ELJ77ceRD7IbUnmuy8t6GKhR/vYNuhE5xdXIBt62CynFlMmV3Hkn2b+J8lmzhrQHdO65nlse3GiuP8/ct9vLehikN1Ta32fdW4PsydNChSpxJXJJTQOyRB1eTeD8l113mKtc3mFnnwdJ2Y1/MWeDMWi6f7xhCivXs9e8P68+V70xXEOlS6Yp8BX+68vXuhoUEtb2qKrfaYmvpm/vDBt/xrzX4aWhwM6JHBiz8a75XyN5updWO46LGPuffV9Tx0eQkOp2RjRS1/W7WX1XuOkWKzcP6QnkwvKWBscTfyMpOpbbDjlJLCnPhMOBYJEkronU6JNYg3Nu+HBFTDa2Ojmj7nHNWJyYgGGDNGCYiRajgYWlpU3vmSEvXbECKbTX2CGSxEE5jyclUpG+0rXaXB2QjndTrVd14e3Gfqruh0KndkLHCiyc5lT33K/mMNzBpdxDVnFTO2uJtPv3l+Vgq/mDGMea9+w7TH3MF5/fLS+eUlw7hqXF9y0pM8tukqsfAdIaGukN0psQaRp9jb5z1njhLzW29VD8gnn6iPQUoK3HWXEpCvv/ZMTNYWzc0qJUJ9vWdD7803t/vUND7wFSnVlSpPw+iQUkWYeb9hvvaaCu+NdqW3bl8Nu4/U89j3R/HdMX0Crn/V+L6U9Mnh24MnsAjon5fBGYXZ2u/eARJK6FUKhOAaqQyftxH1smqVZ2OsmaYm1bsWlJiccYZ7cO5ALFniHtdVCCVEY8a4R4vSse2hY7yZGSJvbldJdMrKPBv/Kypar7Njh6oIo31/bThwHKBdw+8NLchmaEF2ZxWpy5FQQu+Qkppjol2NVMb8QBjWk8MBmzapXq1GLHxbeFceZ56p3gr0gBgdx/vNrKuIPLQ+9xtvhLVr1dumYVS0d9D4UGi2O3l+5U5WbjvMsfoWWhxOuqcn45SSlCQLI3rnULalmt65aeRntT9sUhMeEkronU7J4WrhM8eLd288o/HKmG+kSAjGSofg3TdmpFQuofJydSzoWq6GcNOVQ0d9nbsx1GRenvuN0Wbr3PQXt/35K/69+SAj++TQOzeNJKvg6Mlmkq0W6hrtvLBiF3an5D/O9h8uqel8hAy2dTGCjB8/Xq5evbrd201+ZDl90nJ5fd4Yvxa9zeZ+3TWsHindWS5zc9WD8u678OabwTe+tpfLLguckkGjCZXycmXQvPiiu+G/M1w4I+9/n9N7ZfGvW8/xubzZ7uTwiSYKslO1j72TEUKs8TfeR0L1CbY7JL16Cj78EB54wH1jG9bPAw/A9de7wySN1ABGlsv//V9l/ZSUQEZGx0W+rXbht97SIq/pPEpLVXoN414PZwbThmYH9/xzHZMfWU5to51j9f59n8k2C0W5aVrko0xiuW6kSlPsK+bcmFderhpAjXhjM3Y7PPts68FBQmHSJNVo+2yrDEAKh0NZXFroNZ1FZ6S/qKhp4DfvbGLp+iqmndGLK8f24bIxvTu+Y02nklBC72soQeMVFlS0y5EjKr79D3/w74/3J/IWCxQVwf79gctyxhnqeG1RVRV4PxpNqIS7DeN4fQvfWfAJdY12/uPsfjxw2YhwFFMTARJK6J1SerwifrzSwaXXH6PFLmmuysH5bDIWi/LNB9vo6rF/Z3Ain+Tqz/H1160HADf/XrpU54jXdC7h7FGdkmQhxWYlt3sSv549PDw71USEhBJ6u1Nicwn95zuPcPs76+h+hfLRSIegbk1/jn08FByBmya6d4ejR30tMVS7bZ/jc8+pBl6r1fMNoV8/2LPH3SCsQys18UJqkpWLzujFX1ftZXNlHWcU6Tj3eCGhhP54QwtL1lXgcEr+/uU+emaks+8v42g+aSNj+AGyJ+wipaiGQ2+MxXnSf14MiwVGjPDsHWvNbKT7tPWk9j8MUlC/tZCj/x6ObG59Ce12JeROJwwdClu2uK343bvdx9ChlZp4Y9LgHvx11V6aHX56F2pikoQS+ryMFA6faOK1rw4wc2Qh/zN7BJumJrl89D1I6ZnPErmO3j9ejv1oJk0HcqldPRD7sQzTXiSnnV9FzegdFA2x03SgGwhJ2qBDCIuTE+uKEUkOMkfuA4uTI2/7d8RLqXrQertvulovTk380mx30tDiICcticYWB2v31QCQnmyNbsE07SKhhP4fPz6bFodkUH4GNqt7ABK3mBZxa1UW//XiHj7dX0/myP1kjtl7anzZpqpsbDkNNKW10CMzk8rdGaQNqEZKaNzdg+OfDablsEqdastoIrPvcY74KIdZ1H2lVbBatchrYp9DdY1c+IePqW200yMzhaMnm3BKOGdQHoPyM6NdPE07SCihHxjEzTekIItX/2sE5eXw3J8aeXPjXrJKtwGQlZTC8J65/Mf0bswe3ZtVXwhmz4bqavf2gwerzldyTDrv7TxEz9lfU7epEEddKvbaNJz1ybTlv7dY4MkntchrYp+a+hZqG+2kJlmYMiSfopxURvbJ5fyhPVtFt2lim4A9Y4UQqajBvFNQFcO/pJS/EkKMQo0TmwnsBq6RUtb62P5i1EhUVtQQg78LVKhQe8aGgpHsbNJkycRzfN+8ixapMWGvuEIN+A1qEIRL/riy1bqOhiSaK3Op31rAyU29kXb1imukWHjySfc+NJrO5ujJZlbtOkpdYwslfXLalShMSsnNi9fw780HObN/N+bPGMbY4m6dWFpNR2irZ2wwQi+ADCnlCSFEErASuAt4ArhHSvmxEOIGYICU8j6vba3At8BFwH7gS+BqKeWmto4ZSaHvCFJKjp5s5v0Vjdzy00ZIbyC5Vy0pfY6S1P0k9tpUhtWM4aop3U8NGagteU2k+PZgHZc//RknmtxhX7dOGcS9Fw8Neh92h5NXPt/Dwk92cvhEE3/8wRimlxR2RnE1HaQtoQ9mzFgJnHD9THJ9JDAEZekDLAPeB+7z2nwCsN01dixCiL8Bs4E2hT5eEEKQl5nCD6enMCA351THFCklf/7wCGsy1nOw55dc8r1J9M5Ni3ZxNV2ILVW1/Oyf32AR8M9bSumRmcLTy7fzTNkONhw4TmaKjeLu6dx43gB6ZvmPQLNZLfxo4gCmDOnJlEfL+OuX+7TQxyFB+ehdlvka4DTgKSnlF0KIDcAs4E3gKqCvj017A/tMv/cDZ/k5xlxgLkBxcXGw5Y8ZPBt9Beec04Ndhydw/qNl/Gv1fu66cHA0i6fpIry3oZJFn+zkq701pCdbeejyEs7s3x2AX80azvbqE+ysPklaspUPNh3krXUVPH/dmQFj4l/6dBcAN583oNPPQRN+gkpqJqV0SClHA32ACUKIEcANwO1CiDVAFuArs5Evp7dPX5GUcpGUcryUcnx+fvADFMQyA3pkMLQgi399tY99R+ujXRxNgrNy22FueeUrqk808ctLhvHpvRcwe7Q7D01mio3Xb5vIpz+/gH//dDJv3j4Rp4Q5L67yeX/uO1rP3iNq/gebDjIoP4PSgXmt1tPEPu3KXimlrAHKgIullFuklNOklOOAvwI7fGyyH09Lvw/gYyycxOW33x1BTX0Ls55cyYpt7vCd8nJ46CH1rdF0lHX7arj2hS8YlJ/Be3dN4qbzBtItI7nNbUb0zuGVmybQ2OLg2he+oKFZ5QVpcTiZ9691nPfwciY9spwb/u9LctKS2FF9ku8tLOd4fQiDMWiiSkChF0LkCyFyXdNpwIXAFiFET9c8C/BLVASON18Cg4UQA4QQycAPgCVhKntcMK5fd5bccS75WSn8xwureGr5dj77TDJ1qhrMeepULfaajrGj+gSzn/oUgMd/MIaMlOCjpu1OyYkmO3uO1LPzsGqKe/2rA/xj9X5umDiAn1x4Omv2HGNLVR0AX+2tObWeJn4I5o4oBF52+ektwD+klG8LIe4SQtzuWuc14CUAIUQRKoxyhpTSLoS4A9VQawVelFJuDP9pxDYDemTwxu0Tmf/aeh55fytjUuw0Nw/VQwlqOswn31Zz8+LV5KQl8cw1YxnROyfobauON3LxghUAPPb9UQwvyqHyeAN//mIP+Vkp3DdzGEIIbjpvACu3H2b7oRMMK8xijA6xjDuCibr5BmjVz19K+TgqPt57fgUww/R7KbC0Y8WMf9KTbTz2vdEALFm7g5TsgTTVJut8N5oO8fY3FdidkvfvnkRBjv/oGV802R1YLQKHU/Lx1mpe++oAn24/jNUi+M1lIxCuLuMZKTa+M7yA7+iElXFLQo0wFetYLIJzT+uBBP7yD7vHKFgaTSiM6J2DwympaQhihHsv+uVlsPLe87lyXB9Wbj/CgZoGbpk8iI/+cwrfPzP+It80/kmoFAjxQL2rwat0goXZF0a5MJq45kSTnbKt1SRbLSH30yjMSePRq0aFuWSaWEMLfYQ5vZdKira4fA//b+pgkm36pUoTHOXl8MFyO4199rCv+TBr99VwosnOz74zhKzUpGgXTxPDaKGPMGcP7M7s0UU8uXw7T5dtpyg3jeLu6fTvkcEZhdmU9M5hSEEWqUltp4FtcTipOt5Ii8NJj6wUsvWD7oGRwyhR0k6Ul8Mlt+8n49zNWGub6ZeTzazRRXx/fF9G9c2NdvE0MY4W+ggjhGDB90czc2QR6/fXsOdoPXuO1PP2ugr+8sVeAJJtFib0786ovjn06ZZOis2C3Sk5cqKZLVW1bK2qY2f1yVODP1gElPTOYVy/7lw6qpDeuWn0yEzxGFaxK7FoEdxxhxrBKyUlMdpB/r3cQdbUb7AfT+Pwa2fy49tzmf/daJdKEy9ooY8CQgguOqMXF53R69Q8KSX7jzWw4cBxVu85xqfbD/NM2Q6cXv2Ie+emMaQgi8mn5zMoP5Nkm4Ud1Sd44qPtrNt/nBddXdWzUmycd3oPZo/uzXeGF0Ty9KJKeTncfrt7+MamJsmbH9ZzKK2GjRW1pCdbuXBYr3aFIcYCU6dYWPSnNGzd6skeu4fzJmWjYyk0wRIwe2U0iJfslZ1Ni8PJobomWuxOrBZBTnqSXxdNY4uDHdUnOFjbyIGaRjYeOM6HWw5RXdfEM9eMTfhEVM12JxsrjvPCyw6eezwVkuxkluwnfVgF1jTVkzPZZqHF4UQAr982Me5cHu8ub+SPy7exuXkv15xVzG+/WxLtImliiA6lKY4GWujDQ4vDydQ/fIwQMH/6MMb160Z+Vkq0ixV2ninbwZMfbeOkK6LJQNotDM8p4LqL8yjpncvgXplsrapj5hMr+fXs4cwp7R+dAneAxhYHQ+97D4BdD804Feuu0XQoTbEmfkmyWnjgshH87J/ruOWVNQDkZ6XwkwtP54dnBR8n7XRKdh05ycaKWr6tqmPboTp2VJ+krrGF7hkpnDMoj8vH9mZ4UWTdIfuO1vP4h9v415r9TD49n6sn9CU7LYmVaxrZslnwgyn5TJvsme9l/7EGgLhz3RikJlkZWpDFlqo6DtY2tbuTlKZrooU+wZl8ej4r772Ar/ceY0NFLa9/vZ/739rIxSMK6B4g6dV7G6p45uMdbDxwHLurscBqEfTLS2dQfibd05PZX1PPnz7fwwsrd3FJSSFjinMpzEmjICeVgpxUemalkGQNjy9ZSsnS9VW8u6GSTZW17Kw+SbLVwo8nDWTexUNPDW93ziD/+zh6UnUs2n7wRNyOlpSRYqMwJzUh3840nYMW+i5Ass3CWQPzOGtgHucN7sG0xz7hV0s28r3xfcjPSiE/M4W8TE/R2FRRyy2vrGFgfgY3nTeQgT0yGNE7h0E9M0ixeYZ+1ja28GzZDl7+bDfvrK/0WCYEFGSnMrQgi4vOKODCM3q2OdBFWzy4dDPPrdhFr+wURvXJ5apxfZk1uqhdnYUuH9ubJesO8Ms3NnDOaXn06ZYeUlmiSVaqDbtTotyu2nWjCYz20XdB7ntjA3/6fI/HvFF9cjjntB5MHNSDfnnpPPz+Vt7fWMUX86cGTHdrIKXkeEMLlccbqTreSFVtI5U1DeyvaWD17mPsPVqPRcD0EYU8eHkJOWnuhuVmu5PqE03kpCWR6SP74ld7j3HlM59x1bi+PHh5SYcGpzbG+/3j1WOYNaoo5P1Eiw82VjH3T2v475lncMO5eiAQjUL76DUePHDZCH5y0elsrarjWH0zO6tP8OGWQzz3yU6eKXMPK/Cjc/oHLfKgwkZz05PJTU9mWKHniEVSSr49eII31h5g4cc7KMhJ5b6ZZ3C8oYXfvbuFJWsPcLJZJdkaW5xL/7wMmuxO6psd1Da0sGbvMQpz0rh3+tAOiTzAoPxMLAK2H4rPdLsXndGLMcW5/HPNfi30mqDQQt9F6Z6RTOkg92hBd1wwmJNNdlbvOcbB2kb65KZxdhhHExJCMKQgi3svHsqWylr+VL6HqtpGvth5lKMnm7hsTG8m9O/O3qP1rNh2mE+3HybJZiE92UZakoW5kwZy07kDArYrBINq0Mxm6fpKbpsyKGAv5GixatdR3vmmgpfL9zC0IItrzu5HTloSh2ob2XbwBIPyM6JdRE2coF03mohzqK6Rh9/bSvmOI/TLS2f+9GGU9IlsFMyHmw9y48urGdAjg1unDGL6iIKYyRcjpWTAfJXZOz3ZeioRnpnhRdk8c804ivPir41B0znoOHqNxgcff1vNQ0s3s6WqjmSrhcG9MklNspKaZCHZaiHZZmFUX9XoG8kIlzV7jnLFM+UU5aTy0T1TSE2y4nRKDtU1UdfYQnZaEr2ydVilxpMO+eiFEKnAJ0CKa/1/SSl/JYQYjRo+MBWwA7dJKVf52H43UAc4ALu/gmg0kWby6flMGtyD1XuO8f6GKnYePkmz3UmT3UFtg536ZjvvbzzIY8u+ZebIIu6/dDg56Z1v9R87qXryPnPtuFNuJYtFnApZ1WjaSzA++ibgAinlCSFEErBSCPEu8Gvgf6SU7wohZgAPA1P87ON8KeXhsJRYowkjQgjO7N+dM/t397l8R/UJXvl8D698vod1+2p48Udn0r9H5/rG31lfSYrNQv887YPXhIeAPVmkwghPSHJ9pOtjhFbkABWdUkKNJooMys/kV5cO5883nc2x+mYue/pT3lpXgcM721wYKdt6iJkjiyLy9qDpGgTVZVEIYRVCrAUOAcuklF8AdwOPCCH2AY8C8/1sLoEPhBBrhBBz2zjGXCHEaiHE6urq6vacg0bT6UwY0J3Xb5tIUU4ad/71a8568N/81+vr2VpVF9bjrN59lGP1LQzulRnW/Wq6NkEJvZTSIaUcDfQBJgghRgC3Aj+RUvYFfgK84GfziVLKscB04HYhxCQ/x1gkpRwvpRyfn5/f3vPQaDqd/j0yeOvOc3n6mrGcPTCP177azxXPfEZFTUNY9i+l5LkVO8lMsXFdHCZc08Qu7Yqjl1LWCCHKgIuB64C7XIv+CTzvZ5sK1/chIcTrwARU465GE3dYLYIZJYXMKClk+6E6LvzfT3hrXQU/ntxGgh0XDc0ONlUeZ8OBWrZU1ZGfmYxDSo6ebKGmvlkNKHP4JPdMO5205NiM7dfEJ8FE3eQDLS6RTwMuBH6P8slPBsqAC4BtPrbNACxSyjrX9DRUI65GE/ec1jOLkX1yWFy+h5mjPHPuHKprZP3+43yz/zhbqmrZdvAEu4+cPDWQTE5aErWNLViEoFt6ErnpyRTnpXPLlEFcNa5PlM5Ik6gEY9EXAi8LIawoV88/pJRvCyFqgMeFEDagEZgLIIQoAp6XUs4AegGvu3Jm24C/SCnfC/9paDTR4Z5pQ7j+/75k4u8+IivFRm5GEnWNdmrqVYikRSiXz+m9spg5qoiS3jmM6J1NQXYqzQ4nyVaLzimv6XR0hymNpoPsPVLP+xurOFDTQE19M5mpNvrnZTCqby7Di7JJT9aZRjSdj05qptF0IsV56dw8aWC0i6HR+EWPLqzRaDQJjhZ6jUajSXC00Gs0Gk2Co4Veo9FoEhwt9BqNRpPgaKHXaDSaBEcLvUaj0SQ4Wug1Go0mwYnJnrFCiGpgD9AD0AOW+EZfG//oa+MbfV38kwjXpp+U0mfq35gUegMhxGo99KBv9LXxj742vtHXxT+Jfm2060aj0WgSHC30Go1Gk+DEutAvinYBYhh9bfyjr41v9HXxT0Jfm5j20Ws0Go2m48S6Ra/RaDSaDhKTQi+EGC2E+FwIsVYIsVoIMcE1v78QosE1f60Q4tlolzWS+LsupuXFQogTQoh7olXGaNHGPTPBdL+sE0J8N9pljTRtXJuLhBBrhBDrXd8XRLuskaaNa5MnhFjuep6ejHY5O4yUMuY+wAfAdNf0DKDMNd0f2BDt8sXadTEtfxU1UPs90S5rrFwbIB2wuaYLgUPG767yaePajAGKXNMjgAPRLmsMXZsM4FzgFuDJaJezo59YHWFKAtmu6RzUQOSaNq6LEOIyYCdwMvLFigl8XhspZb1pnVTXel0Nf9fma9M6G4FUIUSKlLIpwuWLJv6uzUlgpRDitGgVLJzEZGOsEGIY8D4gUO6lc6SUe4QQ/VE35LdALfBLKeWKqBU0wrRxXTKAfwMXAfcAJ6SUj0avpJHH37VxLTsLeBHoB/yHlPL1qBU0CrR1bUzrXAncIqW8MApFjBqBro0Q4kfAeCnlHdEpYXiImkUvhPg3UOBj0S+AqcBPpJSvCiG+B7wAXAhUAsVSyiNCiHHAG0KI4VLK2ogVvJMJ8br8D/CYlPKEECJyhY0wIV4bpJRfAMNdD/XLQoh3pZSNkSp3JAj12ri2HQ78HpgWibJGmo5cm0QhVi3640CulFIKpVzHpZTZPtYrQ/mjV0e6jNHA33URQqwA+rpWywWcwH9LKeO/ESlI2nHPLAd+1lXuGWj72ggh+gAfAddLKT+NZjmjQaD7JlEs+piMukH5ySa7pi8AtgEIIfKFEFbX9EBgMMov3VXweV2klOdJKftLKfsDC4AHu5LIu/B3zwwQQthc0/2AIcDuaBQwivi7NrnAO8D8rijyLnxem0QjVhtjbwYedz2gjcBc1/xJwK+FEHbAgfIpHo1SGaOBv+ui8X9tzgV+LoRoQb3p3CaljPcshe3F37W5AzgNuE8IcZ9r3jQp5aEolDFa+H2mhBC7UQ21ya5gh2lSyk3RKGRHiUnXjUaj0WjCR6y6bjQajUYTJrTQazQaTYKjhV6j0WgSHC30Go1Gk+BooddoNJoERwu9RqPRJDha6DUajSbB0UKv0Wg0Cc7/B8hBctwuQ+8UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a plot of total Professional and agricultural workers vs. tract pop\")\n",
    "plt.scatter(tractPop,totProf,label=\"totProf\")\n",
    "plt.scatter(tractPop, totAg,label=\"totAg\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"and here's one with pctage by tract Pop\")\n",
    "plt.scatter(tractPop,pctProf,label=\"pctProf\")\n",
    "plt.scatter(tractPop, pctAg,label=\"pctAg\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"and here's where in the state we see high Ag (red) and high Prof (blue) pctages\")\n",
    "plotPoly(enactedMAP)\n",
    "minAg = 0.05\n",
    "minProf = 0.15\n",
    "for t in range(nTracts):\n",
    "    if pctAg[t] > minAg:\n",
    "        plt.scatter(tractGeom[t].centroid.x, tractGeom[t].centroid.y,marker='.',c=\"red\")\n",
    "    if pctProf[t] > minProf:        \n",
    "        plt.scatter(tractGeom[t].centroid.x, tractGeom[t].centroid.y,marker='.',c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "print(\"and here's where in the state we see high pct Bachelor or higher\")\n",
    "plotPoly(enactedMAP)\n",
    "minBach = 0.25\n",
    "for t in range(nTracts):\n",
    "    if pctBach[t] > minBach:\n",
    "        plt.scatter(tractGeom[t].centroid.x, tractGeom[t].centroid.y,marker='.',c=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85530d60-aa91-4659-9a17-ef1228c11fe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's the correlation of pct in Professional fields (y) vs. pct Bachelor or higher (x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here's the correlation of pct in Professional fields (y) vs. pct Bachelor or higher (x)\")\n",
    "plt.scatter(pctBach,pctProf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empty precinct no 901 at x,y = -89.55910072733401 37.24445253608286\n",
      "empty precinct no 1606 at x,y = -94.49210914633345 39.08843488965521\n",
      "empty precinct no 1639 at x,y = -94.26617739378824 38.9271564921918\n",
      "empty precinct no 1654 at x,y = -94.26459707730935 38.919939308730925\n",
      "empty precinct no 1730 at x,y = -94.27435416256266 38.93014356350911\n",
      "empty precinct no 1818 at x,y = -94.27246433850559 38.84587752027375\n",
      "empty precinct no 1824 at x,y = -94.56599481665683 39.10806043544211\n",
      "empty precinct no 1890 at x,y = -90.40517764210722 38.689422094649736\n",
      "empty precinct no 2133 at x,y = -90.42099529715092 38.695164779590144\n",
      "empty precinct no 2137 at x,y = -90.44222771851216 38.55148223051913\n",
      "empty precinct no 2190 at x,y = -90.45894631248497 38.67486900659456\n",
      "empty precinct no 2210 at x,y = -90.29728720350532 38.66284312599078\n",
      "empty precinct no 2220 at x,y = -90.34319420021899 38.78634877672203\n",
      "empty precinct no 2245 at x,y = -90.32319254390981 38.59764785189998\n",
      "empty precinct no 2267 at x,y = -90.28440885592823 38.63843268013326\n",
      "empty precinct no 2299 at x,y = -90.25916596142659 38.5881597091719\n",
      "empty precinct no 2423 at x,y = -90.27080370795156 38.59526831556846\n",
      "empty precinct no 2515 at x,y = -90.2672530456795 38.563423953214226\n",
      "empty precinct no 2530 at x,y = -90.28418724299746 38.62243064628761\n",
      "empty precinct no 2570 at x,y = -90.27793102450009 38.62301619397034\n",
      "empty precinct no 2573 at x,y = -90.22909573620952 38.7009361465793\n",
      "empty precinct no 2586 at x,y = -90.30107460902332 38.56961041288809\n",
      "empty precinct no 2600 at x,y = -90.26038662520597 38.589668889202834\n",
      "empty precinct no 2623 at x,y = -90.26976662087935 38.623847001149336\n",
      "empty precinct no 2707 at x,y = -90.50220328270025 38.542344893332434\n",
      "empty precinct no 2759 at x,y = -90.22707425054398 38.767405271950594\n",
      "empty precinct no 2857 at x,y = -90.37926065647147 38.611792099841765\n",
      "empty precinct no 2858 at x,y = -90.69796801833708 38.49364155782515\n",
      "empty precinct no 2861 at x,y = -90.48597227665047 38.54278781205807\n",
      "empty precinct no 2862 at x,y = -90.27867084428603 38.809819791507124\n",
      "empty precinct no 2863 at x,y = -90.58415119203669 38.57861910864621\n",
      "empty precinct no 2865 at x,y = -90.2888042434632 38.796343024609\n",
      "empty precinct no 2866 at x,y = -90.27910355297982 38.798186713638316\n",
      "empty precinct no 2867 at x,y = -90.3172939054122 38.82166162059355\n",
      "empty precinct no 2868 at x,y = -90.37938904172262 38.611435972765186\n",
      "empty precinct no 2871 at x,y = -90.33604841605096 38.797938288712906\n",
      "empty precinct no 2872 at x,y = -90.33325383473898 38.799620840394496\n",
      "empty precinct no 2889 at x,y = -90.38084041611971 38.7453628932831\n",
      "empty precinct no 2890 at x,y = -90.3849848110205 38.742414501273124\n",
      "empty precinct no 2891 at x,y = -90.46734337104347 38.555930381994315\n",
      "empty precinct no 2892 at x,y = -90.43807253396511 38.716986342058895\n",
      "empty precinct no 2893 at x,y = -90.39063200926039 38.733844984005614\n",
      "empty precinct no 2894 at x,y = -90.27891951863899 38.73094502194012\n",
      "empty precinct no 2895 at x,y = -90.43683299621644 38.74502273704003\n",
      "empty precinct no 2896 at x,y = -90.38366343314732 38.74510575367041\n",
      "empty precinct no 2897 at x,y = -90.47910977864157 38.72155953823591\n",
      "empty precinct no 2898 at x,y = -90.28613268467235 38.72397198365036\n",
      "empty precinct no 2899 at x,y = -90.36815818455908 38.74020467465513\n",
      "empty precinct no 2900 at x,y = -90.36244272484596 38.73916654423323\n",
      "empty precinct no 2901 at x,y = -90.39676690308511 38.77175325753707\n",
      "empty precinct no 2902 at x,y = -90.39213250420212 38.77757444188208\n",
      "empty precinct no 2903 at x,y = -90.278792227599 38.72438498117348\n",
      "empty precinct no 2904 at x,y = -90.49703096691623 38.727459348124405\n",
      "empty precinct no 2905 at x,y = -90.37988501291034 38.765164385598005\n",
      "empty precinct no 2906 at x,y = -90.41289733530218 38.76620849258644\n",
      "empty precinct no 2907 at x,y = -90.38186912831777 38.777308863539545\n",
      "empty precinct no 2908 at x,y = -90.50982587867476 38.53707722982634\n",
      "empty precinct no 2909 at x,y = -90.36435489551832 38.762675070536815\n",
      "empty precinct no 2910 at x,y = -90.19149776273422 38.76455030466595\n",
      "empty precinct no 2911 at x,y = -90.40549942523634 38.767803120586535\n",
      "empty precinct no 2912 at x,y = -90.34558219536142 38.768650206090705\n",
      "empty precinct no 2913 at x,y = -90.3791807696614 38.75631290118991\n",
      "empty precinct no 2914 at x,y = -90.3632358753636 38.75039017859501\n",
      "empty precinct no 2915 at x,y = -90.39304313872006 38.76518584878731\n",
      "empty precinct no 2916 at x,y = -90.42031981442241 38.76455359068767\n",
      "empty precinct no 2917 at x,y = -90.34456407244514 38.62758669860366\n",
      "empty precinct no 2918 at x,y = -90.4760417525427 38.70553210975663\n",
      "empty precinct no 2919 at x,y = -90.3803881162177 38.758429478134246\n",
      "empty precinct no 2920 at x,y = -90.35211595357586 38.764364078812854\n",
      "empty precinct no 2921 at x,y = -90.47313825596113 38.56703771354142\n",
      "empty precinct no 2922 at x,y = -90.58432027710751 38.5760331872352\n",
      "empty precinct no 2923 at x,y = -90.47409536748897 38.63975811028615\n",
      "empty precinct no 2924 at x,y = -90.4680741995402 38.64267214996268\n",
      "empty precinct no 2925 at x,y = -90.28383959882244 38.545876535554484\n",
      "empty precinct no 2926 at x,y = -90.2843427118143 38.54670015334884\n",
      "empty precinct no 2927 at x,y = -90.3883861299367 38.6029140295877\n",
      "empty precinct no 2928 at x,y = -90.45099132228007 38.60586215549275\n",
      "empty precinct no 2929 at x,y = -90.47549299350908 38.539860590140805\n",
      "empty precinct no 2930 at x,y = -90.30127452001811 38.54161200301598\n",
      "empty precinct no 2931 at x,y = -90.50007905779646 38.54705910775478\n",
      "empty precinct no 2932 at x,y = -90.39785535506176 38.56018040671257\n",
      "empty precinct no 2933 at x,y = -90.3090872131742 38.71976636125504\n",
      "empty precinct no 2934 at x,y = -90.28540388353237 38.719518905399994\n",
      "empty precinct no 2935 at x,y = -90.28225243841894 38.545333384385124\n",
      "empty precinct no 2936 at x,y = -90.4774463548059 38.5426526712364\n",
      "empty precinct no 2937 at x,y = -90.4104605868716 38.69597185064098\n",
      "empty precinct no 2938 at x,y = -90.41519935668737 38.69703542357842\n",
      "empty precinct no 2939 at x,y = -90.31141525274757 38.72026442065424\n",
      "empty precinct no 2940 at x,y = -90.29360411721972 38.72082146509218\n",
      "empty precinct no 2941 at x,y = -90.32951552812519 38.683248493003916\n",
      "empty precinct no 2942 at x,y = -90.29723621301677 38.68174565697948\n",
      "empty precinct no 2943 at x,y = -90.33807664742365 38.69997583594998\n",
      "empty precinct no 2944 at x,y = -90.33543271474865 38.71016702461532\n",
      "empty precinct no 2945 at x,y = -90.51027867772282 38.64754871153683\n",
      "empty precinct no 2946 at x,y = -90.48162797179992 38.653739098119445\n",
      "empty precinct no 2947 at x,y = -90.39882974193875 38.68425046971384\n",
      "empty precinct no 2948 at x,y = -90.39344609302859 38.68908854924592\n",
      "empty precinct no 2949 at x,y = -90.37925493935155 38.750261465707496\n",
      "empty precinct no 2950 at x,y = -90.38455198580186 38.75182217182059\n",
      "empty precinct no 2951 at x,y = -90.45226951728942 38.67306870322649\n",
      "empty precinct no 2952 at x,y = -90.46519529825731 38.678323348264\n",
      "empty precinct no 2953 at x,y = -90.42322542755767 38.50856272780873\n",
      "empty precinct no 2954 at x,y = -90.43469937562622 38.546066967359224\n",
      "empty precinct no 2955 at x,y = -90.29929549987939 38.5402630246964\n",
      "empty precinct no 2956 at x,y = -90.34215035221537 38.53150256829216\n",
      "empty precinct no 2957 at x,y = -90.2849728393104 38.71139843436007\n",
      "empty precinct no 2958 at x,y = -90.31215016438443 38.761530057504594\n",
      "empty precinct no 2959 at x,y = -90.40963536791796 38.5049778234931\n",
      "empty precinct no 2960 at x,y = -90.42127917352649 38.508286302987734\n",
      "empty precinct no 2961 at x,y = -90.49819433823893 38.569152644309185\n",
      "empty precinct no 2962 at x,y = -90.26942598447403 38.70383677313849\n",
      "empty precinct no 2963 at x,y = -90.31232830437617 38.76155054877163\n",
      "empty precinct no 2964 at x,y = -90.49784636746617 38.57045382997997\n",
      "empty precinct no 2965 at x,y = -90.60220655872212 38.507632768894005\n",
      "empty precinct no 2966 at x,y = -90.34297293353278 38.519517038460236\n",
      "empty precinct no 2967 at x,y = -90.27486147149408 38.70696103095068\n",
      "empty precinct no 2968 at x,y = -90.28152545774829 38.709749902181564\n",
      "empty precinct no 2969 at x,y = -90.44100069231786 38.74795304414497\n",
      "empty precinct no 2970 at x,y = -90.44513779500592 38.72380182169869\n",
      "empty precinct no 2971 at x,y = -90.68553813566243 38.52948594812787\n",
      "empty precinct no 2972 at x,y = -90.33948306155446 38.53521651313227\n",
      "empty precinct no 2973 at x,y = -90.50540251785529 38.65902313541245\n",
      "empty precinct no 2974 at x,y = -90.60807705280152 38.644194724344366\n",
      "empty precinct no 2975 at x,y = -90.51150516066248 38.619487521816204\n",
      "empty precinct no 2976 at x,y = -90.38258758905515 38.685831965104555\n",
      "empty precinct no 2977 at x,y = -90.3588015533421 38.679167427216626\n",
      "empty precinct no 2978 at x,y = -90.36158084985038 38.67174727447494\n",
      "empty precinct no 2979 at x,y = -90.42485842589174 38.76594925727032\n",
      "empty precinct no 2980 at x,y = -90.43988648071212 38.74271041954138\n",
      "empty precinct no 2983 at x,y = -90.39207524081571 38.56889284467205\n",
      "empty precinct no 2984 at x,y = -90.39132731393971 38.57132707522988\n",
      "empty precinct no 2985 at x,y = -90.4379973060505 38.55289434879545\n",
      "empty precinct no 2986 at x,y = -90.44271088361783 38.55895582037952\n",
      "empty precinct no 2987 at x,y = -90.3290617160633 38.51931850033854\n",
      "empty precinct no 2988 at x,y = -90.40189143839754 38.560385222465726\n",
      "empty precinct no 2989 at x,y = -90.5001007894694 38.65288641380625\n",
      "empty precinct no 2990 at x,y = -90.28405643563006 38.80829950114725\n",
      "empty precinct no 2991 at x,y = -90.30748402368411 38.822234128526155\n",
      "empty precinct no 2992 at x,y = -90.30415834581541 38.7199345056781\n",
      "empty precinct no 2993 at x,y = -90.51282842075709 38.53523133769996\n",
      "empty precinct no 2994 at x,y = -90.51266408745778 38.53612223492737\n",
      "empty precinct no 2995 at x,y = -90.28245153735861 38.808362465293456\n",
      "empty precinct no 2996 at x,y = -90.28070432434349 38.80928480251615\n",
      "empty precinct no 2997 at x,y = -90.394914916231 38.567797746186116\n",
      "empty precinct no 2998 at x,y = -90.38570172754378 38.57065850312071\n",
      "empty precinct no 2999 at x,y = -90.33350556724606 38.77558995198507\n",
      "empty precinct no 3000 at x,y = -90.42146939027978 38.6990773687762\n",
      "empty precinct no 3001 at x,y = -90.24044608303885 38.77146627585927\n",
      "empty precinct no 3002 at x,y = -90.2373412723215 38.77139784793322\n",
      "empty precinct no 3003 at x,y = -90.38137204076824 38.57451573514502\n",
      "empty precinct no 3004 at x,y = -90.37724047963867 38.57692720225095\n",
      "empty precinct no 3005 at x,y = -90.35367547740721 38.828951307171415\n",
      "empty precinct no 3006 at x,y = -90.34855009772279 38.82198252970071\n",
      "empty precinct no 3007 at x,y = -90.40594792347747 38.62046086595952\n",
      "empty precinct no 3008 at x,y = -90.69484115800964 38.50344328877911\n",
      "empty precinct no 3009 at x,y = -90.56712004625383 38.586735201081\n",
      "empty precinct no 3010 at x,y = -90.29644430960852 38.708826937168666\n",
      "empty precinct no 3011 at x,y = -90.30253504286577 38.80789535085819\n",
      "empty precinct no 3012 at x,y = -90.51048001913642 38.60599605880004\n",
      "empty precinct no 3013 at x,y = -90.35183284975254 38.76123490589203\n",
      "empty precinct no 3014 at x,y = -90.35081274890186 38.76443740155144\n",
      "empty precinct no 3015 at x,y = -90.6030538295782 38.62625330773007\n",
      "empty precinct no 3016 at x,y = -90.60568824043445 38.63532812880527\n",
      "empty precinct no 3017 at x,y = -90.49395906970632 38.691761868256755\n",
      "empty precinct no 3018 at x,y = -90.47361057780985 38.52591907152421\n",
      "empty precinct no 3019 at x,y = -90.39777914787197 38.56191466781594\n",
      "empty precinct no 3020 at x,y = -90.51013794201855 38.53610789494833\n",
      "empty precinct no 3021 at x,y = -90.46345967434367 38.69950592675766\n",
      "empty precinct no 3022 at x,y = -90.36262346394915 38.616463221874604\n",
      "empty precinct no 3023 at x,y = -90.47335308722406 38.642584643030574\n",
      "empty precinct no 3024 at x,y = -90.4286924168412 38.7492019477414\n",
      "empty precinct no 3025 at x,y = -90.51185350695297 38.61327507574522\n",
      "empty precinct no 3026 at x,y = -90.43592510853146 38.74647148299795\n",
      "empty precinct no 3027 at x,y = -90.42340078073997 38.748004840656115\n",
      "empty precinct no 3028 at x,y = -90.37774980172135 38.78324962468766\n",
      "empty precinct no 3029 at x,y = -90.4625411207581 38.578551255415576\n",
      "empty precinct no 3030 at x,y = -90.56326157267203 38.59273445104806\n",
      "empty precinct no 3031 at x,y = -90.51251049206817 38.535347534263316\n",
      "empty precinct no 3032 at x,y = -90.3983150058087 38.55762216521723\n",
      "empty precinct no 3033 at x,y = -90.39096918453515 38.757461173766394\n",
      "empty precinct no 3034 at x,y = -90.43493484173176 38.528522275782514\n",
      "empty precinct no 3035 at x,y = -90.51351060008359 38.53190285259118\n",
      "empty precinct no 3036 at x,y = -90.39447970437193 38.67305314312069\n",
      "empty precinct no 3037 at x,y = -90.60837280112776 38.54286785335794\n",
      "empty precinct no 3038 at x,y = -90.37231641115102 38.65117663026849\n",
      "empty precinct no 3039 at x,y = -90.3954390866833 38.636279826689574\n",
      "empty precinct no 3040 at x,y = -90.41508356820711 38.71672666177734\n",
      "empty precinct no 3041 at x,y = -90.37473847985699 38.459158807728066\n",
      "empty precinct no 3042 at x,y = -90.47075820910231 38.575692216018304\n",
      "empty precinct no 3043 at x,y = -90.41836974737811 38.71827105068594\n",
      "empty precinct no 3044 at x,y = -90.51379638051345 38.53463552963391\n",
      "empty precinct no 3045 at x,y = -90.67593938191965 38.6705353303042\n",
      "empty precinct no 3046 at x,y = -90.51213185452619 38.53749565126183\n",
      "empty precinct no 3047 at x,y = -90.44979315380806 38.5573068827592\n",
      "empty precinct no 3048 at x,y = -90.36131317556486 38.67559856468616\n",
      "empty precinct no 3049 at x,y = -90.34753572620122 38.559379570420326\n",
      "empty precinct no 3050 at x,y = -90.30325982633012 38.772236809495254\n",
      "empty precinct no 3051 at x,y = -90.27913243236831 38.784214951522564\n",
      "empty precinct no 3052 at x,y = -90.36770314765754 38.79859792985938\n",
      "empty precinct no 3053 at x,y = -90.52228452950631 38.59228944655801\n",
      "empty precinct no 3054 at x,y = -90.34359207420934 38.54059663414011\n",
      "empty precinct no 3055 at x,y = -90.62620275179613 38.65935345348123\n",
      "empty precinct no 3056 at x,y = -90.35096511753923 38.491278479827415\n",
      "empty precinct no 3057 at x,y = -90.34919624699062 38.772572713341305\n",
      "empty precinct no 3058 at x,y = -90.52623854083782 38.62082822954578\n",
      "empty precinct no 3059 at x,y = -90.52889587084583 38.5982650830631\n",
      "empty precinct no 3060 at x,y = -90.52819601370688 38.59303567884145\n",
      "empty precinct no 3061 at x,y = -90.39092563042763 38.6026001037483\n",
      "empty precinct no 3062 at x,y = -90.46083536632831 38.69984144722004\n",
      "empty precinct no 3063 at x,y = -90.52014675013939 38.622660089666404\n",
      "empty precinct no 3064 at x,y = -90.34952770032397 38.777302385982686\n",
      "empty precinct no 3065 at x,y = -90.51569008682736 38.644924181867374\n",
      "empty precinct no 3066 at x,y = -90.4692318847439 38.67874281357123\n",
      "empty precinct no 3067 at x,y = -90.4688218055221 38.678556505952386\n",
      "empty precinct no 3068 at x,y = -90.33966091402688 38.5133007526557\n",
      "empty precinct no 3069 at x,y = -90.36807843116436 38.78002282486859\n",
      "empty precinct no 3070 at x,y = -90.37418588157868 38.6023487319768\n",
      "empty precinct no 3071 at x,y = -90.33372411026652 38.59173993247568\n",
      "empty precinct no 3072 at x,y = -90.47024472347678 38.60013010982108\n",
      "empty precinct no 3073 at x,y = -90.45471761301387 38.60500670649198\n",
      "empty precinct no 3074 at x,y = -90.35858470918078 38.721927115610896\n",
      "empty precinct no 3075 at x,y = -90.36481010070598 38.78045855966655\n",
      "empty precinct no 3076 at x,y = -90.36266228118761 38.600911080339294\n",
      "empty precinct no 3077 at x,y = -90.59929008193606 38.57807592209109\n",
      "empty precinct no 3078 at x,y = -90.3178680942792 38.69810293603101\n",
      "empty precinct no 3079 at x,y = -90.35782672449002 38.60970225918305\n",
      "empty precinct no 3080 at x,y = -90.30082525682683 38.81199452615202\n",
      "empty precinct no 3081 at x,y = -90.67771001604399 38.6626011041939\n",
      "empty precinct no 3088 at x,y = -90.37447221233359 38.68333323622852\n",
      "empty precinct no 3089 at x,y = -90.25230194699746 38.74704575896135\n",
      "empty precinct no 3092 at x,y = -90.3420378151138 38.81895763533359\n",
      "empty precinct no 3093 at x,y = -90.34945579185057 38.77782677614194\n",
      "empty precinct no 3097 at x,y = -90.70381778263894 38.49111129085358\n",
      "empty precinct no 3099 at x,y = -90.3615489317342 38.82676687691735\n",
      "empty precinct no 3100 at x,y = -90.45133023343405 38.772421151546084\n",
      "empty precinct no 3102 at x,y = -90.58269685826589 38.621013421538386\n",
      "empty precinct no 3103 at x,y = -90.58210624172969 38.62192191055267\n",
      "empty precinct no 3104 at x,y = -90.35693277649473 38.83108538078187\n",
      "empty precinct no 3107 at x,y = -90.62971105693286 38.687183294826326\n",
      "empty precinct no 3119 at x,y = -90.29993494876764 38.661791516319376\n",
      "empty precinct no 3130 at x,y = -90.45384312275901 38.782481156774715\n",
      "empty precinct no 3133 at x,y = -90.4651688412057 38.77271505585309\n",
      "empty precinct no 3164 at x,y = -90.52892888962137 38.61718172693703\n",
      "empty precinct no 3360 at x,y = -90.42805080650982 38.703614185179504\n",
      "empty precinct no 3365 at x,y = -90.47360920267853 38.68936996908371\n",
      "empty precinct no 3428 at x,y = -90.35290868362817 38.68434082096871\n",
      "empty precinct no 3452 at x,y = -90.46672119210301 38.7020414253464\n",
      "empty precinct no 3600 at x,y = -90.24753048838132 38.736862946699496\n",
      "empty precinct no 3635 at x,y = -90.24064992970779 38.75042097602459\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "isEmptyVTD = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p]\n",
    "    if vtdPop[p] == 0:\n",
    "        isEmptyVTD == 1\n",
    "        print(\"empty precinct no\",p,\"at x,y =\",vtdCPx[p],vtdCPy[p])\n",
    "    else:\n",
    "        vtdGOP[p] = vtdTrump[p]/(vtdTrump[p] + vtdBiden[p])\n",
    "        if (vtdGOP[p] > 0.40) :\n",
    "            plotcolor[p]='purple'\n",
    "            if (vtdGOP[p] > 0.60) :\n",
    "                plotcolor[p] = 'red'\n",
    "        if p%10 == 0: #memory drain if we print all\n",
    "            plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "\n",
    "for t in range(nDistricts):\n",
    "    plotPoly(enactedGeom[t])\n",
    "    plt.text(enactedGeom[t].centroid.x,enactedGeom[t].centroid.y,t+1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e61b3350-8b0b-4e29-9a40-f87635f58dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for GA and Florida, there are invalid precinct geometries.  Convert them to their convex hulls\n",
    "fixList = [3343,3283,1732,3280,1729,1737,1736, 163,404,188,5661,2561,2583, 2589,\n",
    "           2592,1733, 167, 432,2828,2777,2830,2802]  #fill in as needed here  #this is the Florida list\n",
    "\n",
    "for p in fixList:\n",
    "    vtdGeom[p] = vtdGeom[p].convex_hull\n",
    "    #vtdGeom[2034] = vtdGeom[2034].convex_hull  #GA had only one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f1650bda-ee8c-470c-b604-599ad5f68d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(enactedGeom[3])\n",
    "POINT1 = Point(-92.52,38.35)\n",
    "POINT2 = Point(-92.3, 38.35)\n",
    "POINT3 = Point(-92.3,38.5)\n",
    "POINT4 = Point(-92.52, 38.5)\n",
    "healPoly = Polygon([POINT1, POINT2, POINT3, POINT4])\n",
    "plotPoly(healPoly)\n",
    "plt.show()\n",
    "new3 = enactedGeom[3].union(healPoly)\n",
    "plotPoly(new3)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "99b6bbbc-524f-42b1-b844-4d526d2afdf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "enactedGeom[3]=new3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2d6097eb-ac2f-4be6-b35a-795a60efcc6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on district 1\n",
      "working on district 2\n",
      "working on district 3\n",
      "working on district 4\n",
      "working on district 5\n",
      "working on district 6\n",
      "working on district 7\n",
      "working on district 8\n",
      "sum tractPop was 6154913 sum of our district pops was 6147833.4810784105\n"
     ]
    }
   ],
   "source": [
    "#3/18/22 basic computation of district population and R/D lean of the enacted map\n",
    "# (we calculate population as a sanity check on our tractPop averaging technique)\n",
    "\n",
    "tractEDUse = [0.]* nTracts  #how much this tract is used across the entire enacted map\n",
    "precinctEDUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "\n",
    "EDvPop = [0.]*nDistricts\n",
    "EDvGOP = [0.]*nDistricts    #GOP lean of each enacted district\n",
    "EDtotGOP = [0.]*nDistricts  #how many total Trump voters in each district\n",
    "EDtotVote = [0.]*nDistricts  #how many Trump + Biden voters \"  \"\n",
    "EDvBlack = [0.]*nDistricts   #VAP pct Black by district\n",
    "EDvHisp = [0.]*nDistricts #VAP pct Hispanic bydistrict\n",
    "EDarea = [0.]*nDistricts  #geographical area of each enacted district\n",
    "EDtotVAP = [0.]*nDistricts\n",
    "\n",
    "\n",
    "for d in range(nDistricts) :  #loop on each enacted district\n",
    "    print(\"working on district\",d+1)\n",
    "    EDarea[d] = enactedGeom[d].area\n",
    "    for t in range(nTracts):\n",
    "        if (t+1)%2000 == 0:\n",
    "            print(\"working on tract\",t+1)\n",
    "        overlap = tractGeom[t].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/tractArea[t]\n",
    "            EDvPop[d] += tractPop[t]*fracArea\n",
    "            EDtotVAP[d] += tractVAP[t]*fracArea\n",
    "            EDvBlack[d] += tractBlack[t]*fracArea\n",
    "            EDvHisp[d] += tractHisp[t]*fracArea         \n",
    "            \n",
    "    EDvBlack[d]   = EDvBlack[d] / EDtotVAP[d]  #normalize these totals by pop in the district\n",
    "    EDvHisp[d]    = EDvHisp[d] / EDtotVAP[d]\n",
    "\n",
    "    \n",
    "    for p in range(nPrecincts):\n",
    "        if (p+1)%5000 == 0:\n",
    "            print(\"working on precinct\",p+1)\n",
    "        overlap = vtdGeom[p].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/vtdArea[p]\n",
    "            precinctEDUse[p] += fracArea\n",
    "            EDtotVote[d] += fracArea*(vtdTrump[p]+vtdBiden[p])\n",
    "            EDtotGOP[d] += fracArea*vtdTrump[p]\n",
    "    EDvGOP[d] = EDtotGOP[d] / EDtotVote[d]\n",
    "\n",
    "statePop = np.sum(tractPop)\n",
    "stateEDPop = np.sum(EDvPop)\n",
    "print(\"sum tractPop was\",statePop,\"sum of our district pops was\",stateEDPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on district 1\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 2\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 3\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 4\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 5\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 6\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 7\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 8\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 9\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 10\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 11\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 12\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 13\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 14\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "working on district 15\n",
      "working on tract 2000\n",
      "working on precinct 5000\n",
      "sum tractPop was 11799448 sum of our district pops was 11799245.327372476\n"
     ]
    }
   ],
   "source": [
    "#7/22 ALT TO ABOVE - more detailed enacted demographics\n",
    "# (we calculate population as a sanity check on our tractPop averaging technique)\n",
    "\n",
    "tractEDUse = [0.]* nTracts  #how much this tract is used across the entire enacted map\n",
    "precinctEDUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "\n",
    "EDvPop = [0.]*nDistricts\n",
    "EDvGOP = [0.]*nDistricts    #GOP lean of each enacted district\n",
    "EDtotGOP = [0.]*nDistricts  #how many total Trump voters in each district\n",
    "EDtotVote = [0.]*nDistricts  #how many Trump + Biden voters \"  \"\n",
    "EDvBlack = [0.]*nDistricts   #VAP pct Black by district\n",
    "EDvHisp = [0.]*nDistricts #VAP pct Hispanic bydistrict\n",
    "EDarea = [0.]*nDistricts  #geographical area of each enacted district\n",
    "EDtotVAP = [0.]*nDistricts\n",
    "EDworkerPop = [0.]*nDistricts  #total adult employed population in the district\n",
    "EDpctProf = [0.]*nDistricts\n",
    "EDpctAg = [0.]*nDistricts\n",
    "EDpctBach = [0.]*nDistricts\n",
    "\n",
    "for d in range(nDistricts) :  #loop on each enacted district\n",
    "    print(\"working on district\",d+1)\n",
    "    EDarea[d] = enactedGeom[d].area\n",
    "    for t in range(nTracts):\n",
    "        if (t+1)%2000 == 0:\n",
    "            print(\"working on tract\",t+1)\n",
    "        overlap = tractGeom[t].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/tractArea[t]\n",
    "            tractEDUse[t] += fracArea\n",
    "            EDvPop[d] += tractPop[t]*fracArea\n",
    "            EDtotVAP[d] += tractVAP[t]*fracArea\n",
    "            EDworkerPop[d] += workerPop[t]*fracArea\n",
    "            EDvBlack[d] += tractBlack[t]*fracArea\n",
    "            EDvHisp[d] += tractHisp[t]*fracArea\n",
    "            EDpctProf[d] += totProf[t]*fracArea \n",
    "            EDpctAg[d] += totAg[t]*fracArea\n",
    "            EDpctBach[d] += totBach[t]*fracArea          \n",
    "            \n",
    "    EDvBlack[d]   = EDvBlack[d] / EDtotVAP[d]  #normalize these totals by pop in the district\n",
    "    EDvHisp[d]    = EDvHisp[d] / EDtotVAP[d]\n",
    "    EDpctProf[d]  = EDpctProf[d] / EDworkerPop[d]\n",
    "    EDpctAg[d]    = EDpctAg[d] / EDworkerPop[d]\n",
    "    EDpctBach[d]  = EDpctBach[d] / EDworkerPop[d]\n",
    "    \n",
    "    for p in range(nPrecincts):\n",
    "        if (p+1)%5000 == 0:\n",
    "            print(\"working on precinct\",p+1)\n",
    "        overlap = vtdGeom[p].intersection(enactedGeom[d]).area\n",
    "        if overlap > 0 :\n",
    "            fracArea = overlap/vtdArea[p]\n",
    "            precinctEDUse[p] += fracArea\n",
    "            EDtotVote[d] += fracArea*(vtdTrump[p]+vtdBiden[p])\n",
    "            EDtotGOP[d] += fracArea*vtdTrump[p]\n",
    "    EDvGOP[d] = EDtotGOP[d] / EDtotVote[d]\n",
    "\n",
    "statePop = np.sum(tractPop)\n",
    "stateEDPop = np.sum(EDvPop)\n",
    "print(\"sum tractPop was\",statePop,\"sum of our district pops was\",stateEDPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f93187a1-7f19-4da2-a845-8e564da519fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 762220.819 0.711 = districtNo, Pop, pctGOP\n",
      "2 761380.432 0.578 = districtNo, Pop, pctGOP\n",
      "3 769435.341 0.536 = districtNo, Pop, pctGOP\n",
      "4 775594.283 0.596 = districtNo, Pop, pctGOP\n",
      "5 768515.496 0.665 = districtNo, Pop, pctGOP\n",
      "6 770415.963 0.798 = districtNo, Pop, pctGOP\n",
      "7 768299.594 0.536 = districtNo, Pop, pctGOP\n",
      "8 771971.554 0.202 = districtNo, Pop, pctGOP\n"
     ]
    }
   ],
   "source": [
    "for d in range(nDistricts):    \n",
    "    print(d+1,r3(EDvPop[d]),r3(EDvGOP[d]),\"= districtNo, Pop, pctGOP\")\n",
    "    # print(d+1,r3(EDvPop[d]),r3(EDvGOP[d]),r3(EDpctProf[d]),r3(EDpctAg[d]),r3(EDpctBach[d]), \n",
    "    #       \"= districtNo, Pop, pctGOP, pctProf, pctAg, pctBach\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "67330128-5329-4e14-a59b-1f868a0368e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a scatter plot of district-wide ACS stats vs. partisan lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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SaljpsISWZ4N6/JIvFOiSVpUTKykpKum0rKSohMqJlVmqqLP2Hv+h5kM43tHjV6hLGCnQJa1mXjaT6qnVDC8djmEMLx1O9dTqnPlCVD1+ySc6ykXSbuZlM3MmwLtSj1/yiUboUtC66+WbmXrqEjoKdClo8Xr8AJ/4J+qpS+go0KWgde3xn2fn/kmopy5hoR66FLzYHn/FExVx11FPXcJAI3SRGGE4bl6kOwp0kRi5fty8SE/UchGJ0d56qdlew+HmwwwrHUblxMqcPewyDGoba/V6ZogCXaSLXD5uPmw022ZmBWq5mNkMM9tjZnvN7IEe1ptkZmfMbE7qShSRsNKZuJnVa6CbWRHwKHAHMBaYb2Zju1nvh8DGVBcpIuGkM3EzK8gIfTKw190b3f1jYA0wO856S4B1wN9SWF8nmhVPJFx01FBmBQn0MuCdmNtNbcs6mFkZcDewMnWldaZZ8UTCR0cNZVaQQLc4y7zL7eXA/e5+pscHMltoZvVmVn/kyJGAJUapFycSPrk+22a+CXKUSxMwIuZ2OXCwyzoRYI2ZAQwBvmRmre6+PnYld18FrAKIRCJddwo9Ui9OJJx01FDmBBmh1wGXm9loM+sPzAOejV3B3Ue7+yh3HwU8DfzPrmGeLPXiRER61mugu3srsJjo0StvAE+5+24zW2Rmi9JdYDv14iQs9OW9ZEugE4vcfQOwocuyuF+Auvv/SL6sc+kMPgkDnUgj2WTuCbWyUyYSiXh9fX1WnlskXaY9PY1DzYfOWT68dDib5mzKQkWSb8xsm7tH4t2nyblEUkhf3ks2KdBFUkhf3ks2KdBFUkhf3ks2abZFkRTSl/eSTQp0kRTTiTSSLWq5iIjkCQW6iEieUKCLiOQJBbqISJ5QoIuI5AkFuohInlCgi4jkCQW6iEieUKCLhITmWZfe6ExRkRDQPOsShEboIiGgi6RLEAp0kRDQPOsShAJdJATCOs+6+v6ZpUAXCYEwzrPe3vc/1HwIxzv6/gr19FGgh5BGPdmRzdd95mUzqZ5azfDS4RjG8NLhVE+tzukvRNX3z7xAR7mY2QygBigCHnf3h7vcPxt4CPgEaAW+5e4vp7hWQUc7ZEsuvO5hm2ddff/M63WEbmZFwKPAHcBYYL6Zje2y2gvAOHcfD3wdeDzFdUobjXqyQ6974sLa9w+zIC2XycBed29094+BNcDs2BXc/SN397abpYAjaaFRT3bodU9cGPv+YRck0MuAd2JuN7Ut68TM7jazN4FaoqP0c5jZQjOrN7P6I0eO9KXegqdRT3bodU9cGPv+YRck0C3OsnNG4O7+G3e/CriLaD/93B9yX+XuEXePDB06NKFCJUqjnuzQ6943My+byaY5m2hY0MCmOZsU5mkW5EvRJmBEzO1y4GB3K7v7S2Y2xsyGuPt7yRYonemq8tmh113CwM62vrtZwawf8BZwG3AAqAPuc/fdMet8Bnjb3d3MJgLPAeXew4NHIhGvr69PwSaIiBQOM9vm7pF49/U6Qnf3VjNbDGwketjianffbWaL2u5fCfwT8FUzOw20AHN7CnMREUm9Xkfo6aIRuohI4noaoetMURGRPKFAFxHJEwp0EQk9zW8UpSsWiUio5cI8O7lCI3QRCTXNs3OWAl1EQi2b8+wk2upJd2tILRcRCbVhpcM41Hwo7vJ0SrTVk4nWkEboIhJqycyzk8yIOdFWTyZaQxqhi0io9XWenWRHzIm2ejLRGlKgi0jo9eVqTj2NmIM8VqKtnky0htRyEZGClOyIOdFWTyamYNYIXUQKUrIj5kRbPZmYglmTc4lIQeraQ4foiDnXr6qU1PS5IiL5KB8vWqJAF5GC1ZcvU3OZvhQVEckTCnQRkTyhQBcRyRMKdBGRPKFAFxHJE4EC3cxmmNkeM9trZg/Euf+fzayh7d8rZjYu9aWKiEhPeg10MysCHgXuAMYC881sbJfV/gLc7O4VwEPAqlQXKiIiPQsyQp8M7HX3Rnf/GFgDzI5dwd1fcfdjbTe3AuWpLVNERHoTJNDLgHdibje1LevOvwC/TaYoERFJXJAzRS3OsrgTwJjZLUQD/cZu7l8ILAQYOXJkwBJFRCSIICP0JmBEzO1y4GDXlcysAngcmO3uR+M9kLuvcveIu0eGDh3al3pFRKQbQQK9DrjczEabWX9gHvBs7ApmNhJ4BviKu7+V+jJFRKQ3vbZc3L3VzBYDG4EiYLW77zazRW33rwSqgEuAn5kZQGt30zuKiEh6aD50EZEQ6Wk+dJ0pKiKSJxToIiJ5QoEuIpInFOgiInlCgS4ikicU6CIieUKBLiKSJxToIiJ5QoEuIpInFOgiInlCgS4ikicU6CIieUKBLunT8BT857VQPTj634ansl2RSF4LcsUikcQ1PAXPfRNOt0Rvn3gnehug4t7s1SWSxzRCl/R4YdnZMG93uiW6XETSQoEu6XGiKbHlIpI0Bbqkx6Dy+MvtPPXSRdJEgS7pcVsVFA84d7mfifbSFeoiKadAl/SouBdm/QSs6Nz71EsXSQsFehiF5XDAinvBP4l/n3rpIimnQA+b9sMBT7wD+NnDAXM11LvrpXe3PBvCsoMMK72+GRMo0M1shpntMbO9ZvZAnPuvMrMtZvZ3M/t26suUDmE7HDBeL714QHR5LgjbDjJs9PpmVK+BbmZFwKPAHcBYYL6Zje2y2vvAN4H/SHmF0lnYDgds76UPGgFY9L+zfpI7Jxd1t4P8zaK+jyg1Ij0rbAOQkAtypuhkYK+7NwKY2RpgNvB6+wru/jfgb2Y2My1VylmDyttGO3GW56qKe3MnwLvqbkfoZ9ruT/AMV50h21nYBiAhF6TlUgbEJkhT27LM08gn91sYYRNkR5jIiFIj0s7C8B1KHgkS6BZnmfflycxsoZnVm1n9kSNHEvth9eKicr2FETbdHS/fVdARpUaknWkAklFBWi5NwIiY2+XAwb48mbuvAlYBRCKRxHYKPY18Ci3McrmFETbtr+MLy6Kha+edbbfECjqiDGNLLJ26vr6DyqNhrt/ftAgS6HXA5WY2GjgAzAPuS2tV8WjkI+kSu4Ps2gOHxEaUt1Ul9/P5SAOQjOk10N291cwWAxuBImC1u+82s0Vt9680s2FAPTAQ+MTMvgWMdfcPUlapRj6SCcmOKDUilSwy9z61w5MWiUS8vr4++A90N3JS/1gKRcNT4dtRhLHmHGdm29w9Eu++8FzgQiMfybZshlMYD4cMY80hF54Rukg2ZfsT4n9e203LcQT822vpf/6+CGPNicrCTr6nEbrmchEJItvHl4fxoIBM1pyNc1Ry8FBqBbpIENkO1DCeoJOpmpMJ1mR2BNneycehQBcJItuBGsYTdDJVc1+DNdkRdl928mn+JKFAFwki24EaxjOEM1VzXz89JTvCTnQnn4EWTXiOchHJplw4yiqMJ+hkoua+nqOSbBst0ZPIMnC2uwJdJKgwBmoh6OvZucmerJjoTj4D38Mo0EUk3Pr66SkV0zQkspPPwNnuCnQRCb++fHrKdBstA/P8KNBFpHBlso2WgR2IAl1EJFPSvANRoItIwVq/4wCPbNzDweMtXDp4AEunX8ldE7JzQbZUUKCLSEFav+MADz7zKi2noxc0OXC8hQefeRUgtKGuE4tEpCA9snFPR5i3azl9hkc27slSRcnTCF1ECtLB4y0JLU+FdLd4FOgikjaZ6lH35XkuHTyAA3HC+9LBAS4a3sca093iUctFRNKiPcAOHG/BORtg63ccyInnWTr9SgYUF3VaNqC4iKXTr0zouW94+EVGP1DLDQ+/2ONzZqLFo0AXCSiRP17JXI+6r89z14Qy/v0fr6Ns8AAMKBs8gH//x+sCj5YT3ZFkosWjlotIALlwRETYDrHLVI86mee5a0JZn1/DnnYk8R4zEy0eBbqkTVgCqGudt1w1lN+/eaRT3Yn88aZju3Nhh5Ko7gJs0IDijDxPunrh7RLdkSydfmWn9xASb/H0JlDLxcxmmNkeM9trZg/Eud/M7Cdt9zeY2cSUVRhDH3mjwvA6xPs4+q21Oxn/g005VW+8Ov/P1v3nfIyOFxhw7h9vuvrGYTzEbun0Kyk+z85Z3vxxa0p/B1LRC++L7nYY3S1PtsUTRK+BbmZFwKPAHcBYYL6Zje2y2h3A5W3/FgL/O2UVtsnUFyy5LiyvQ7wAAjjecjqn6u2uzlgtp89QZOcGE5z7x5uu4M3GIXbJumtCGReUnNsEOH3GU7ojykRQxtOXHcldE8r40wO38peHZ/KnB25NeY1BWi6Tgb3u3ghgZmuA2cDrMevMBn7l7g5sNbPBZjbc3Q+lqtBE+1X5KiyvQ09Bk0v1Bg3EM+4MKC7q9eNyuoI3W22FZB0/eTru8lTviJLphSfznEBOtRWDBHoZEDuJbxNwfYB1yoBOgW5mC4mO4Bk5cmRChYZxhJIOYXkdugugdrlSb291tiuL6aX39MebruDNRP81HcK6IwoqGzuSngTpocf7rOl9WAd3X+XuEXePDB06NEh9HRLtV+WrsLwO8T6OxsqVenurE84GZ5CPy+nq52arrZCsbPW3C1WQEXoTMCLmdjlwsA/rJCWsI5RUC8vr0B40P3huN8e6fOzOpXrjfWyOd5RL0OBM58fwXBsNBpGLbYl8ZtG2dw8rmPUD3gJuAw4AdcB97r47Zp2ZwGLgS0TbMT9x98k9PW4kEvH6+vqEig3LYXDpFrbXIWz1iuQyM9vm7pG49/UW6G0P8CVgOVAErHb3/2VmiwDcfaWZGbACmAGcBL7m7j2mdV8CXUSk0PUU6IFOLHL3DcCGLstWxvy/A99IpkgREUmO5nIREckTCnQRkTyhQBcRyRMKdBGRPBHoKJe0PLHZEeCvwBDgvawUkTmFsI1QGNtZCNsIhbGdYd3Gf3D3uGdmZi3QOwowq+/uEJx8UQjbCIWxnYWwjVAY25mP26iWi4hInlCgi4jkiVwI9FXZLiADCmEboTC2sxC2EQpjO/NuG7PeQxcRkdTIhRG6iIikQEYCPcA1Sf+57VqkDWb2ipmNy0RdqRZgO2e3beNOM6s3sxuzUWcyetvGmPUmmdkZM5uTyfpSJcB7+QUzO9H2Xu40s6ps1JmMIO9l23buNLPdZvZ/M11jKgR4L5fGvI+vtf3eXpyNWpPm7mn9R3SGxreBy4D+wC5gbJd1pgIXtf3/HcCf011XlrbzAs62uSqAN7Ndd6q3MWa9F4lO6DYn23Wn6b38AvDf2a41zds4mOilJke23f50tutOx3Z2WX8W8GK26+7rv0yM0DuuSeruHwPt1yTt4O6vuPuxtptbiV4gI2yCbOdH3vZbA5QS56pOOa7XbWyzBFgH/C2TxaVQ0O0MsyDbeB/wjLvvB3D3ML6fib6X84EnM1JZGmQi0Lu73mh3/gX4bVorSo9A22lmd5vZm0At8PUM1ZYqvW6jmZUBdwMrCa+gv7OfM7NdZvZbM7smM6WlTJBtvAK4yMz+YGbbzOyrGasudQLnj5mdT/SaDusyUFdaBJoPPUmBrjcKYGa3EA300PWWCX5d1d8AvzGzzwMPAV9Md2EpFGQblwP3u/uZ6HVPQinIdm4negr2R20XgFkPXJ7uwlIoyDb2Az5L9GplA4AtZrbV3d9Kd3EpFDh/iLZb/uTu76exnrTKRKAHut6omVUAjwN3uPvRDNSVagldV9XdXzKzMWY2xN3DMp9EkG2MAGvawnwI8CUza3X39RmpMDV63U53/yDm/zeY2c/y8L1sAt5z92ag2cxeAsYRvSRlWCTydzmPELdbgIx8KdoPaARGc/ZLiWu6rDMS2AtMzfaXCmnezs9w9kvRiUSv0WrZrj2V29hl/V8Szi9Fg7yXw2Ley8nA/nx7L4GrgRfa1j0feA24Ntu1p3o729YbBLwPlGa75mT+pX2E7u6tZrYY2MjZa5Lujr0mKVAFXAL8rG1k1+ohmzQn4Hb+E/BVMzsNtABzve23KQwCbmPoBdzOOcC/mlkr0fdyXr69l+7+hpk9DzQAnwCPu/tr2as6cQn8zt4NbPLop5HQ0pmiIiJ5QmeKiojkCQW6iEieUKCLiOQJBbqISJ5QoIuI5AkFuohInlCgi4jkCQW6iEie+P+rwkRPkgsjOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a scatter plot of district-wide ACS stats vs. partisan lean\")\n",
    "plt.scatter(EDvGOP, EDpctAg, label=\"pctAg\")\n",
    "plt.scatter(EDvGOP, EDpctProf,label=\"pctProf\")\n",
    "plt.scatter(EDvGOP, EDpctBach, label=\"pctBach\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dc0b1520-7419-4d9a-983e-67dddf0efdeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n",
      "working on tract 2000\n",
      "working on tract 2500\n",
      "working on tract 3000\n",
      "All 3168 home districts built for  OH\n"
     ]
    }
   ],
   "source": [
    "#THIS BLOCK -- PULL IN THE HD OUTPUT FILE, recreate the HD Polygons\n",
    "infilename = \"OH15HD1tol0.005nW413Mar.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "tractPop3 = df2['tractPop']  #these four may include shifting demography out of corners - avoid overwrite for now\n",
    "#tractVAP = df2['tractVAP']   #OOPS!  I forgot to write tractVAP to all my state HD output files!!\n",
    "#                             Need to go back and add tractVAP after clipPoly slicing operation\n",
    "tractHisp = df2['tractHisp']\n",
    "tractBlack = df2['tractBlack']\n",
    "# ... and now the true outputs\n",
    "HDvPop = df2['HD-pop']\n",
    "HDvHisp = df2['HDvHisp']\n",
    "HDvBlack = df2['HDvBlack']\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "HDangle0 = df2['HDangle0']\n",
    "HDangle1 = df2['HDangle1']\n",
    "HDangle2 = df2['HDangle2']\n",
    "HDangle3 = df2['HDangle3']\n",
    "HDradius0 = df2['HDradius0']\n",
    "HDradius1 = df2['HDradius1']\n",
    "HDradius2 = df2['HDradius2']\n",
    "HDradius3 = df2['HDradius3']\n",
    "startAngle = df2['startAngle']\n",
    "\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractNo = df2['tractNo']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "#nTracts = len(tractPop)   #nTracts should be already known from an above code block\n",
    "\n",
    "#Now, recreate each Home District geometry\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1),(1,0)])\n",
    "dummyPoint = Point(0,0)\n",
    "HDPoly = [dummyPoly]*nTracts\n",
    "nWedges = 4\n",
    "Pt = [Point(0,0)]*(nWedges*2)\n",
    "for t in range(nTracts):  #Don't worry, we'll build trivial zero-area HD's for skipped tracts\n",
    "    if t%500 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    HDangle = [ HDangle0[t],HDangle1[t],HDangle2[t],HDangle3[t] ]\n",
    "    HDradius = [ HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    CP = Point( tractCPx[t],tractCPy[t] )\n",
    "    for nW in range(nWedges):\n",
    "        ccwAngle = HDangle[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle = HDangle[ int((nW+1)%4) ]\n",
    "        Pt[nW*2] = Point(tractCPx[t]+HDradius[nW]*math.cos(ccwAngle),\n",
    "                         tractCPy[t]+HDradius[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(tractCPx[t]+HDradius[nW]*math.cos(cwAngle),\n",
    "                           tractCPy[t]+HDradius[nW]*math.sin( cwAngle) )\n",
    "        \n",
    "    HDPoly[t] = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "print(\"All\",nTracts,\"home districts built for \",STATE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dcc0943b-916e-4078-88f5-2c4f09ea9576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now I will calculate agrarian, professional, and Baccalaureate percentages in each Home District\n",
      "working on HD tract 0\n",
      "working on HD tract 50\n",
      "working on HD tract 100\n",
      "working on HD tract 150\n",
      "working on HD tract 200\n",
      "working on HD tract 250\n",
      "working on HD tract 300\n",
      "working on HD tract 350\n",
      "working on HD tract 400\n",
      "working on HD tract 450\n",
      "working on HD tract 500\n",
      "working on HD tract 550\n",
      "working on HD tract 600\n",
      "working on HD tract 650\n",
      "working on HD tract 700\n",
      "working on HD tract 750\n",
      "working on HD tract 800\n",
      "working on HD tract 850\n",
      "working on HD tract 900\n",
      "working on HD tract 950\n",
      "working on HD tract 1000\n",
      "working on HD tract 1050\n",
      "working on HD tract 1100\n",
      "working on HD tract 1150\n",
      "working on HD tract 1200\n",
      "working on HD tract 1250\n",
      "working on HD tract 1300\n",
      "working on HD tract 1350\n",
      "working on HD tract 1400\n",
      "working on HD tract 1450\n",
      "working on HD tract 1500\n",
      "working on HD tract 1550\n",
      "working on HD tract 1600\n",
      "working on HD tract 1650\n",
      "working on HD tract 1700\n",
      "working on HD tract 1750\n",
      "working on HD tract 1800\n",
      "working on HD tract 1850\n",
      "working on HD tract 1900\n",
      "working on HD tract 1950\n",
      "working on HD tract 2000\n",
      "working on HD tract 2050\n",
      "working on HD tract 2100\n",
      "working on HD tract 2150\n",
      "working on HD tract 2200\n",
      "working on HD tract 2250\n",
      "working on HD tract 2300\n",
      "working on HD tract 2350\n",
      "working on HD tract 2400\n",
      "working on HD tract 2450\n",
      "working on HD tract 2500\n",
      "working on HD tract 2550\n",
      "working on HD tract 2600\n",
      "working on HD tract 2650\n",
      "working on HD tract 2700\n",
      "working on HD tract 2750\n",
      "working on HD tract 2800\n",
      "working on HD tract 2850\n",
      "working on HD tract 2900\n",
      "working on HD tract 2950\n",
      "working on HD tract 3000\n",
      "working on HD tract 3050\n",
      "working on HD tract 3100\n",
      "working on HD tract 3150\n"
     ]
    }
   ],
   "source": [
    "#Now we can compute the intensive demographic variables for each Home District, as we did for enacted Districts\n",
    "HDworkerPop = [0.]*nTracts  #total adult employed population in the district\n",
    "HDpctProf = [0.]*nTracts\n",
    "HDpctAg = [0.]*nTracts\n",
    "HDpctBach = [0.]*nTracts\n",
    "print(\"Now I will calculate agrarian, professional, and Baccalaureate percentages in each Home District\")\n",
    "for t in range(nTracts) :  #loop on each Home District\n",
    "    if t%50 == 0:\n",
    "        print(\"working on HD tract\",t)\n",
    "    HDworkerPop[t] = 0.\n",
    "    if (HDPoly[t].intersection(enactedMAP)).area > 0.0001:  #skipping inconsequential tracts\n",
    "        for tt in range(nTracts):\n",
    "            overlapArea = tractGeom[tt].intersection(HDPoly[t]).area \n",
    "            if overlapArea > 0 :\n",
    "                fracArea = overlapArea / tractArea[tt]\n",
    "                HDworkerPop[t] += fracArea* workerPop[tt]\n",
    "                HDpctProf[t] += fracArea * totProf[tt]\n",
    "                HDpctAg[t] += fracArea * totAg[tt]\n",
    "                HDpctBach[t] += fracArea * totBach[tt]\n",
    "        HDpctProf[t] = HDpctProf[t] / HDworkerPop[t]   #normalizing these percentages\n",
    "        HDpctAg[t] =   HDpctAg[t]   / HDworkerPop[t]\n",
    "        HDpctBach[t] = HDpctBach[t] / HDworkerPop[t]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f876175b-7cc4-4de5-9e3f-15310a5cfa62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96 5\n",
      "POLYGON ((-81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327, -81.67383487989999 41.48292163644327))\n"
     ]
    }
   ],
   "source": [
    "#start ~7:58am, ends ~8:15\n",
    "print(t,tractPop[t])\n",
    "print(HDPoly[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "412480e7-749a-4e8c-8cd3-6c89e991f824",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing expected R-D lean and ACS stats for tract 0\n",
      "computing expected R-D lean and ACS stats for tract 100\n",
      "computing expected R-D lean and ACS stats for tract 200\n",
      "computing expected R-D lean and ACS stats for tract 300\n",
      "computing expected R-D lean and ACS stats for tract 400\n",
      "computing expected R-D lean and ACS stats for tract 500\n",
      "computing expected R-D lean and ACS stats for tract 600\n",
      "computing expected R-D lean and ACS stats for tract 700\n",
      "computing expected R-D lean and ACS stats for tract 800\n",
      "computing expected R-D lean and ACS stats for tract 900\n",
      "computing expected R-D lean and ACS stats for tract 1000\n",
      "computing expected R-D lean and ACS stats for tract 1100\n",
      "computing expected R-D lean and ACS stats for tract 1200\n",
      "computing expected R-D lean and ACS stats for tract 1300\n",
      "computing expected R-D lean and ACS stats for tract 1400\n",
      "computing expected R-D lean and ACS stats for tract 1500\n",
      "computing expected R-D lean and ACS stats for tract 1600\n",
      "computing expected R-D lean and ACS stats for tract 1700\n",
      "computing expected R-D lean and ACS stats for tract 1800\n",
      "computing expected R-D lean and ACS stats for tract 1900\n",
      "computing expected R-D lean and ACS stats for tract 2000\n",
      "computing expected R-D lean and ACS stats for tract 2100\n",
      "computing expected R-D lean and ACS stats for tract 2200\n",
      "computing expected R-D lean and ACS stats for tract 2300\n",
      "computing expected R-D lean and ACS stats for tract 2400\n",
      "computing expected R-D lean and ACS stats for tract 2500\n",
      "computing expected R-D lean and ACS stats for tract 2600\n",
      "computing expected R-D lean and ACS stats for tract 2700\n",
      "computing expected R-D lean and ACS stats for tract 2800\n",
      "computing expected R-D lean and ACS stats for tract 2900\n",
      "computing expected R-D lean and ACS stats for tract 3000\n",
      "computing expected R-D lean and ACS stats for tract 3100\n",
      "All done computing avg and std dev of Home-District-expected intensive quantities for each tract\n"
     ]
    }
   ],
   "source": [
    "#7/8/22 - This block: compute the average and std dev in each tract's expected values for the above quantities\n",
    "# To do this, we loop through all Home Districts to find intersections with the tract of interest ...\n",
    "#about 30min for 3200 tracts\n",
    "vGOPavg = [0.]*nTracts   #the most likely R-D lean for a given tract based on the HDs it's drawn into\n",
    "vGOPvar = [0.]*nTracts\n",
    "vGOPsd = [0.]*nTracts\n",
    "pctProfAvg = [0.]*nTracts\n",
    "pctProfVar = [0.]*nTracts\n",
    "pctProfSD = [0.]*nTracts\n",
    "pctAgAvg = [0.]*nTracts\n",
    "pctAgVar = [0.]*nTracts\n",
    "pctAgSD = [0.]*nTracts\n",
    "pctBachAvg = [0.]*nTracts\n",
    "pctBachVar = [0.]*nTracts\n",
    "pctBachSD = [0.]*nTracts\n",
    "\n",
    "for t in range(nTracts):\n",
    "    weight = [0.]*nTracts\n",
    "    if t%100 == 0:\n",
    "        print(\"computing expected R-D lean and ACS stats for tract\",t)\n",
    "    if tractUse[t] > 0.1 :  #skip unused tracts\n",
    "        sumWeight = 0.\n",
    "        for tt in range(nTracts):\n",
    "            intersxnArea = tractGeom[t].intersection(HDPoly[tt]).area\n",
    "            if intersxnArea > 0:\n",
    "                weight[tt] = intersxnArea/tractArea[t] * tractPop[tt]/avgDistrictPop #latter term is HDweight[tt]\n",
    "                sumWeight += weight[tt]\n",
    "                vGOPavg[t] += weight[tt] * HDvGOP[tt]\n",
    "                pctProfAvg[t] += weight[tt] * HDpctProf[tt]\n",
    "                pctAgAvg[t] += weight[tt] * HDpctAg[tt]\n",
    "                pctBachAvg[t] += weight[tt] * HDpctBach[tt]\n",
    "        vGOPavg[t] = vGOPavg[t] / sumWeight\n",
    "        pctProfAvg[t] = pctProfAvg[t] / sumWeight\n",
    "        pctAgAvg[t]   = pctAgAvg[t]   / sumWeight\n",
    "        pctBachAvg[t] = pctBachAvg[t] / sumWeight      \n",
    "        \n",
    "        \n",
    "        for tt in range(nTracts):\n",
    "            weight[tt] = weight[tt] / sumWeight  #renormalizing\n",
    "            vGOPvar[t]    += weight[tt] * (HDvGOP[tt] -    vGOPavg[t]   )**2\n",
    "            pctProfVar[t] += weight[tt] * (HDpctProf[tt] - pctProfAvg[t])**2\n",
    "            pctAgVar[t]   += weight[tt] * (HDpctAg[tt]   - pctAgAvg[t]  )**2\n",
    "            pctBachVar[t] += weight[tt] * (HDpctBach[tt] - pctBachAvg[t])**2\n",
    "        \n",
    "        vGOPsd[t]     = vGOPvar[t]**0.5\n",
    "        pctProfSD[t]  = pctProfVar[t]**0.5\n",
    "        pctAgSD[t]    = pctAgVar[t]**0.5\n",
    "        pctBachSD[t] =  pctBachVar[t]**0.5\n",
    "print(\"All done computing avg and std dev of Home-District-expected intensive quantities for each tract\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4ef45acc-392f-4223-a1c1-cdb0e22330e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "orange is the expected pct GOP that each tract expects to be pulled into\n"
     ]
    },
    {
     "data": {
      "image/png": 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gQzvVLknqxLIE1DHAOw2Wa+rWtdompfRfwCzgbWAjsDml9OTelytJ2l9kCajmpqVuPNa72TYRcTiF3lVv4IvAwRFxYbMvEjE1IqojotqRepKkLAFVAxzbYLkXTU/TtdTmL4E3U0qbUkqfAA8Do5p7kZTSvJRSVUqpqmfPnlnrlyR1UlkCagnQJyJ6R0Q3CoMcFjRqswC4qG403wgKp/I2Uji1NyIiDorCDYLGA2vasX5JUifV6vegUkq1EXEF8ASFUXj3pJRejYjL6rbPBRYCE4F1wFbgkrptL0bEz4BlQC3wMtD0Fq6SJDWS6Yu6KaWFFEKo4bq5DX5OwOUt7HsTcNNnqFGStB8qmbn4JEn7FwNKkpRLBpQkKZdKZrJYSWp3Ld3aXblgD0qSlEsGlCQplwwoSVIuGVCSpFwyoCRJuWRASZJyyYCSJOWSASVJyiUDSpKUSwaUJCmXDChJUi4ZUJKkXDKgJEm5ZEBJknLJgJIk5ZIBJUnKJQNKkpRLBpQkKZcMKElSLnUpdgGSVDJu7r6HbZs7ro79hD0oSVIuGVCSpFwyoCRJuWRASZJyyYCSJOWSASVJyiUDSpKUSwaUJCmXDChJUi4ZUJKkXDKgJEm5ZEBJknLJgJIk5ZIBJUnKJQNKkpRLBpQkKZcMKElSLhlQkqRcMqAkSbmUKaAi4rSIWBsR6yJiejPbIyLm1G1fERFDG2w7LCJ+FhGvRcSaiBjZnm9AktQ5tRpQEVEG3AlMAPoB50dEv0bNJgB96h5TgbsabLsDeDyl1BcYBKxph7olSZ1clh7UcGBdSumNlNIO4EFgcqM2k4H7UsELwGERcXREHAqMAf4VIKW0I6X0/9qvfElSZ5UloI4B3mmwXFO3Lkub44BNwI8j4uWIuDsiDm7uRSJiakRUR0T1pk2bMr8BSVLnlCWgopl1KWObLsBQ4K6U0hBgC9DkGhZASmleSqkqpVTVs2fPDGVJkjqzLAFVAxzbYLkXsCFjmxqgJqX0Yt36n1EILEmS9ihLQC0B+kRE74joBpwHLGjUZgFwUd1ovhHA5pTSxpTSH4B3IuKEunbjgdXtVbwkqfPq0lqDlFJtRFwBPAGUAfeklF6NiMvqts8FFgITgXXAVuCSBk9xJXB/Xbi90WibJEnNajWgAFJKCymEUMN1cxv8nIDLW9h3OVC19yVKkvZHziQhScolA0qSlEsGlCQplwwoSVIuGVCSpFwyoCRJuWRASZJyyYCSJOWSASVJyiUDSpKUSwaUJCmXDChJUi4ZUJKkXDKgJEm5ZEBJknLJgJIk5ZIBJUnKJQNKkpRLBpQkKZcMKElSLhlQkqRcMqAkSblkQEmScsmAkiTlkgElScolA0qSlEsGlCQplwwoSVIudSl2AZK0NyqmP9bitvUzJ3VgJXtWKnXmkQElKX9u7r6HbZs7rg4VlQElqdOx19I5eA1KkpRL9qAkdYjmejXry7/WLs+jzsmAkqR9qKUQrtj+QAdXUnoMKEntw4ENamdeg5Ik5ZIBJUnKJU/xSSopXtPZfxhQkvYrDUcBri8vYiFqlaf4JEm5ZA9K0j7nd5fa194cz1KcQcOAktQ2expOLrUjA0rSPrc3M0aUHIO73WUKqIg4DbgDKAPuTinNbLQ96rZPBLYCU1JKyxpsLwOqgf9KKZ3eTrVLUr3OFoKeFs0wSKIuXO4EJgD9gPMjol+jZhOAPnWPqcBdjbZ/E1jzmauVJO03svSghgPrUkpvAETEg8BkYHWDNpOB+1JKCXghIg6LiKNTShsjohcwCbgd+Hb7li9Jpcte0p5lGWZ+DPBOg+WaunVZ2/wQuA7YuacXiYipEVEdEdWbNm3KUJYkqTPLElDRzLqUpU1EnA78MaW0tLUXSSnNSylVpZSqevbsmaEsSVJnluUUXw1wbIPlXsCGjG3OAc6MiIlAOXBoRPwkpXTh3pcsSZ2b0zkVZOlBLQH6RETviOgGnAcsaNRmAXBRFIwANqeUNqaUbkgp9UopVdTt97ThJEnKotUeVEqpNiKuAJ6gMMz8npTSqxFxWd32ucBCCkPM11EYZn7JvitZkrQ/yPQ9qJTSQgoh1HDd3AY/J+DyVp5jEbCozRVKkvZLThYrScolA0qSlEsGlCQplwwoSVIuGVCSpFwyoCRJuWRASZJyyYCSJOWSASVJyiVv+S5JRdDZ7gC8L9iDkiTlkgElScolA0qSlEsGlCQplwwoSVIuGVCSpFxymLmk5t3cvdgVqJGWhqZXbH+ggyvpGPagJDVRMf2xYpcgGVCSpHwyoCRJuWRASZJyyYCSJOWSASVJyiUDSpKUSwaUJCmXDChJUi45k4RU4vb0pdr1Myd1YCVS+zKgpP2Us0Uo7wwoaT/mbceVZ16DkiTlkj0oqRPzNJ5KmT0oSVIu2YOSOpH97X5B6tzsQUmScskelLQfcLSeSpE9KElSLhlQkqRcMqAkSblkQEmScslBElJe3dy9hfWbO7YOqUjsQUmScskelFRqGvWs1pcXqQ5pH8sUUBFxGnAHUAbcnVKa2Wh71G2fCGwFpqSUlkXEscB9wBeAncC8lNId7Vi/VPpaOpUn7edaPcUXEWXAncAEoB9wfkT0a9RsAtCn7jEVuKtufS3wf1NKJwIjgMub2VeSpCayXIMaDqxLKb2RUtoBPAhMbtRmMnBfKngBOCwijk4pbUwpLQNIKX0ErAGOacf6JUmdVJaAOgZ4p8FyDU1DptU2EVEBDAFebO5FImJqRFRHRPWmTZsylCVJ6syyBFQ0sy61pU1EHAI8BHwrpfRhcy+SUpqXUqpKKVX17NkzQ1mSpM4syyCJGuDYBsu9gA1Z20REVwrhdH9K6eG9L1UqYQ6EkNosSw9qCdAnInpHRDfgPGBBozYLgIuiYASwOaW0sW50378Ca1JKs9u1cklSp9ZqDyqlVBsRVwBPUBhmfk9K6dWIuKxu+1xgIYUh5usoDDO/pG73k4GvAysjYnnduhtTSgvb9V1IkjqdTN+DqguUhY3WzW3wcwIub2a/39D89SlJkvbIqY4kSblkQEmScsm5+KS94Uzj0j5nQEntrGL6Y03WOaGr1HYGlCTtB5r7j9Mu62dO6sBKsvMalCQplwwoSVIueYpPasEeT4l4TUna5wwo7RdaCpv2Pve+p1CT1Dae4pMk5ZIBJUnKJQNKkpRLBpQkKZcMKElSLhlQkqRccpi59mvtPSx8ffnX2vX5pI6Q12mQ7EFJknLJgJIk5ZIBJUnKJa9BqdNwmiGpc7EHJUnKJQNKkpRLBpQkKZcMKElSLhlQkqRcMqAkSblkQEmScsmAkiTlkgElScolA0qSlEsGlCQpl5yLTyXF+fak/YcBJbXAmw9KxeUpPklSLhlQkqRc8hSfcsfrTJLAHpQkKacMKElSLhlQkqRcMqAkSblkQEmScsmAkiTlksPMJUkt2tPXPtbPnLRPXztTDyoiTouItRGxLiKmN7M9ImJO3fYVETE0676SJDWn1YCKiDLgTmAC0A84PyL6NWo2AehT95gK3NWGfSVJaiLLKb7hwLqU0hsAEfEgMBlY3aDNZOC+lFICXoiIwyLiaKAiw77aDzlbhKTWZAmoY4B3GizXACdlaHNMxn0BiIipFHpfAB9HxNoMte1JD+Ddz/gcxVCKdZdizdBK3dGBhbRBpzzWOVYidZ/ecKHDao7vt9tT/XlzK7MEVHO/pyljmyz7FlamNA+Yl6GeTCKiOqVU1V7P11FKse5SrBlKs+5SrBmsuyOVYs0tyRJQNcCxDZZ7ARsytumWYV9JkprIMopvCdAnInpHRDfgPGBBozYLgIvqRvONADanlDZm3FeSpCZa7UGllGoj4grgCaAMuCel9GpEXFa3fS6wEJgIrAO2Apfsad998k6aarfThR2sFOsuxZqhNOsuxZrBujtSKdbcrCgMvJMkKV+c6kiSlEsGlCQpl0o+oD7LNEzFkqHmvhHxfET8KSKuKUaNzclQ9wV1x3hFRPw2IgYVo85GNbVW8+S6epdHRHVEjC5GnY1lnSIsIoZFxKcRcU5H1teSDMd7bERsrjveyyNiRjHqbFRTq8e6ru7lEfFqRCzu6Bqbk+FYX9vgOK+q+5wcUYxa91pKqWQfFAZevA4cR2FI+ytAv0ZtJgK/pPCdrBHAiyVQ81HAMOB24JpiH+c21D0KOLzu5wklcqwP4X+uxVYCr5XCsW7Q7mkKg5TOKYW6gbHAo8WutY01H0Zh9psv1S0fVQp1N2p/BvB0setu66PUe1D10zCllHYAu6ZSaqh+GqaU0gvArmmYiqXVmlNKf0wpLQE+KUaBLchS929TSh/ULb5A4XtvxZSl5o9T3W8wcDAtfJG8g2X5XANcCTwE/LEji9uDrHXnSZaavwY8nFJ6Gwq/nx1cY3PaeqzPB37aIZW1o1IPqJamWGprm46Ut3qyamvd/4dCz7WYMtUcEV+NiNeAx4BLO6i2PWm17og4BvgqMLcD62pN1s/IyIh4JSJ+GRH9O6a0FmWp+Xjg8IhYFBFLI+KiDquuZZl/HyPiIOA0Cv+ZKSmlfj+ozzINU7HkrZ6sMtcdEeMoBFSxr+dkqjml9HPg5xExBrgV+Mt9XVgrstT9Q+D6lNKnEbmZNTBL3cuAP08pfRwRE4FfULgLQrFkqbkL8GVgPHAg8HxEvJBS+t2+Lm4P2vJ35AzguZTS+/uwnn2i1APqs0zDVCx5qyerTHVHRCVwNzAhpfReB9XWkjYd65TSsxHxvyKiR0qpmBOEZqm7CniwLpx6ABMjojal9IsOqbB5rdadUvqwwc8LI+Jfiny8s/4NeTeltAXYEhHPAoOAYgZUWz7b51GCp/eAkh8k0QV4A+jN/1wo7N+ozSR2HyTxUt5rbtD2ZvIzSCLLsf4ShdlERhW73jbU/L/5n0ESQ4H/2rWc57obtb+XfAySyHK8v9DgeA8H3i7m8c5Y84nAr+raHgSsAgbk/VjXtesOvA8cXOzPx948SroHlT7DNEzFkqXmiPgCUA0cCuyMiG9RGKHzYUvPm4e6gRnAkcC/1P3PvjYVcVbljDWfTWEeyU+AbcC5qe43u1gy1p07Ges+B5gWEbUUjvd5xTzeWWpOKa2JiMeBFcBO4O6U0qpi1VxXV9bPyFeBJ1Oh91dynOpIkpRLpT6KT5LUSRlQkqRcMqAkSblkQEmScsmAkiTlkgElScolA0qSlEv/Hx8XC3E5l1PXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight,label=\"Home District lean\")\n",
    "print(\"orange is the expected pct GOP that each tract expects to be pulled into\")\n",
    "ax.hist(vGOPavg,bins=n_bins,weights=HDweight,label=\"expected district lean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aaa24d85-1a5d-4a09-bd5b-e64b40a7850b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "these are histograms of absolute std devns\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"these are histograms of absolute std devns\") \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(vGOPsd, bins=n_bins,weights=HDweight,label=\"sd in expected district lean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0ba10a12-66d1-462c-932b-2d316c5e76ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on relative variance for tract 0\n",
      "working on relative variance for tract 1000\n",
      "working on relative variance for tract 2000\n",
      "working on relative variance for tract 3000\n"
     ]
    }
   ],
   "source": [
    "#Now we can compute each tract's relative variance from expected values for properties x,y,z\n",
    "relDevnGOP = [0.]*nTracts\n",
    "relDevnPctProf = [0.]*nTracts\n",
    "relDevnPctAg = [0.]*nTracts\n",
    "relDevnPctBach = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on relative variance for tract\",t)\n",
    "    if tractUse[t] > 0.1:\n",
    "        for d in range(nDistricts):        \n",
    "            fracOverlap = tractGeom[t].intersection(enactedGeom[d]).area / tractArea[t]\n",
    "            if fracOverlap > 0:\n",
    "                relDevnGOP[t]     += fracOverlap * (EDvGOP[d] - vGOPavg[t]) / vGOPsd[t]            \n",
    "                relDevnPctProf[t] += fracOverlap * (EDpctProf[d] - pctProfAvg[t]) / pctProfSD[t]\n",
    "                relDevnPctAg[t]   += fracOverlap * (EDpctAg[d]   - pctAgAvg[t]) /   pctAgSD[t]\n",
    "                relDevnPctBach[t] += fracOverlap * (EDpctBach[d] - pctBachAvg[t]) / pctBachSD[t]       \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "607acb32-41ac-4cbe-9c11-8bc13c17e0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define a simple Gaussian as a comparison to the observed relative deviations\n",
    "normalDevn = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    normalDevn[t] = np.random.normal(0.,1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "629f3bb1-3ebe-4b8d-a7bb-81301c8433dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a weighted histogram of the distribution of these relative devns from tract-expected R-D leans\n",
      "starting with R-D lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and now pct with Professional degrees\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraction in agriculture, etc\n"
     ]
    },
    {
     "data": {
      "image/png": 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uSXIh8Fzgp5L8dVX91qgnzv0pvqr6RlW9oKqWq2qZ1X/k58y6nEZJsmvN5iXAN2eVZVxJ9gB/CFxSVU/NOs8mNM5txTQFWf3f7CeBw1X10VnnGUeSpadXzib5CeB1zMH3jar6QFVtH3x/vgz4+3HKCTZBQc2xK5Pcm+QeVk9Ptl/mClwFPB+4abA8ft+sA40jya8nWQFeDVyf5MZZZxpmsADl6duKHQY+V1WHZptqtCTXAl8DXppkJcnbZ51pDK8B3gK8dvB3+a7B//A7OxO4efA943ZWr0GNvWR7HnmrI0lSS86gJEktWVCSpJYsKElSSxaUJKklC0qS1JIFJUlqyYKSJLX0f1kDmjEOudLqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and fraction with a Bachelor or higher\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is a weighted histogram of the distribution of these relative devns from tract-expected R-D leans\")\n",
    "print(\"starting with R-D lean\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "n_bins = 50\n",
    "dBin = 0.25\n",
    "maxBin = 4.\n",
    "n_bins = []\n",
    "totBins = int(2*maxBin/dBin)\n",
    "for bb in range(totBins):\n",
    "    BIN = -1.*maxBin + bb * dBin\n",
    "    n_bins.append(BIN)\n",
    "#n_bins = [-3,-2.5,-2,-1.5,-1,-0.5,0,0.5,1,1.5,2,2.5]\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnGOP, bins=n_bins, weights=tractPop)\n",
    "ax.hist(normalDevn, bins=n_bins)  #no need for weighting function for our simulated normal distro\n",
    "plt.show()\n",
    "print(\"and now pct with Professional degrees\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnPctProf, bins=n_bins, weights=tractPop)\n",
    "ax.hist(normalDevn, bins=n_bins)  #no need for weighting function for our simulated normal distro\n",
    "plt.show()\n",
    "print(\"fraction in agriculture, etc\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnPctAg, bins=n_bins, weights=tractPop)\n",
    "plt.show()\n",
    "print(\"and fraction with a Bachelor or higher\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(relDevnPctBach, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5f543744-d3ad-42d5-b32e-f52a04c610a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where R-D lean is 1.5 redder or bluer than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where R-D lean is\",minRelDevn,\"redder or bluer than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnGOP[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "    if relDevnGOP[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "24c47450-1ed9-4b87-acdd-bc006cb4e4fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct Agricultural lean is 1.5 more (red) or less (blue) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where pct Agricultural lean is\",minRelDevn,\"more (red) or less (blue) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctAg[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "    if relDevnPctAg[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0c91574b-4618-4ae4-8845-0093db0a3103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct white-collar is 1.5 less (red) or more (blue) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where pct white-collar is\",minRelDevn,\"less (red) or more (blue) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctProf[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "    if relDevnPctProf[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b5313f9a-e45d-416a-b88a-6233459f67d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is where pct w Bach degree is 1.5 less (red) or more (blue) than HD expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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iGHp0NideMqJLzufda4Pt1fQTCiekd94fqEnXIYRgYmoiE1MTuW9YX5aWVzO/qJwN1e4mp6k3d5sWTWwdl5zAPUNSOSHJzoTl27BkpHeqyAOgxEaP3hB6SeCBB+CrdqTC6t27w2yKBTrLyxp/Qi9Eq1wv7SUhOKjk7sJzmnQdqhBMTU9maifexGs83uCnLvgNxYjrRv12CXACutvbYtsmsVqNAdk4oTMf6uOuxItQOq9KSzgSgr7G1ysqeHv1/q47sUn8EOxld0UHJRRs49+/t/mGnYyybBkAATWCsNbGCnjiiXDjjfDNN6Z/PkLiT+i7uEefmW5naoWgKEHhnX0lLe9gYtKYkJB1wc/W1i9Y8E2J7piQOtioJdCs0AsBqtqw56YoMGMGPPWUKfKtIP5cN0rXCr3FovLO7KMY8emquoEuE5PWUCv0XfC7tRuzgUW0B2OTkqASDvYbi3qqVu8mJ5ESvH47uiMLDhVTt1EgLSq4c+BfHweb112z1P4ZDJt9fBd+i+5DHAp95yUGava8sks6ZCZxSF3EVRf8ghQj2kVGecKU9fgTsb7jZkvSqWwZdmrTDYeGWVcUfDVmg4d1HzzLGX88h8T+8TVI214iFnphzLRYAeyXUs4SQlwE3AccCUyWUq5oYr98oAoIAJqUclJ7jW7BzqiMMylAdIe3TLo7XdJBUYJ/8lEejLWcNosrNz2O95G/g64bRWRnngXDhlG9aRmFPxyk9503YBMpsOZHGD8exowB6t0Ya2+Qgp2fr+GHLckcsA1ly/srOPqWWdH4Wu0nBrJX3gZsAlKCy+uBnwDPRLDvyVLK4lba1iaEItCjEAEjJGwVGnM+WY9NKNxz7CD6ZZgFFUxaQQf9bEMpYTIzYfVqY92ECVBSAiMUCxcAG5bs5Uevn5Jya5OpY0ITVLduBZvNeF13HcyZ0zF2Om69DcekyYflrxEvPEDl4vVkDMnAcfzZcPl5LR5r0/wVpGpFVFiy0f3RfVppM504bBKR0Ash+gFnAw8AdwBIKTcFt3WacW1BUURUXDfDAiqrnTqfCD+aKkhftZcrxvRBYkyakbLuHer+pmXQ5WPcm4LtpAyuk3VtZL3l2nUSvd7xpJS1+x6RnUTfdPNG071o3e+2cSqYkhJD3G+/3Zg0qofptPdN6Mv5dwgmD/yU8fk5lFWkUf50Koc+zCSpTx9IyKBs5RocpVvItaUyr7cb2wAfAU2l2pPIDX98DJjRMWJf/460aJGxbsqUeoZHri1r9qZBMNR52ZZk1l73DgASAYjglRX11hnLMqhf40ZoHHfX+W3/LjFOpD36x4C7gLYEE0tgoRBCAs9IKeeFaySEmAPMARgwYEAbThM6TnSeSj849ygAdpe4OHbtVp5V3Ty7aUfXGxKk71adleccHbXzm7SBFjooIV0sL4ePPoJNmxruIoQRlKLrTR9qv6sfI59Yzo1HPM/pg78mO+UQ/dIPYJf5WIqXA+DMArIgg5oG+2ZQyZVH/I87/tBOoQ89Knz4YZ2ximIMFH/5JbU3PBF5cMO4vmUc2lNJjUwkxaGBBQzJEdT39ITx+rCnKoOivRXt+EKxT4tCL4SYBRRJKVcKIaa34RxTpZQHhBDZwOdCiM1SysPmOQdvAPMAJk2a1OYuuYhSjz7EwMwEHk7OpMDlC/YlgnZR70dW+6rrsSiibp0INhDQoByeIkTtcUTwSKHPIphGQQDPHiimKO6G2XsAzfxu8/IM74avmYJMUkIgAq/F9orh3LnsIVhWT18XakwatJ//PHGAe/85mA3XHENGduVh+/54aBxVVRF8l6bIy4Np0w43VNeRPh/Fj/yK8vzQeSPv0U/7/UVtNumFYO8/nolEDqYC5wohZgIOIEUI8aqU8opITiClPBB8LxJCvAdMBlqZ0CJyjB59dONf/m9S/6ie//0PSikU3dRP2ZOR8rCU66HlH35oXuQbIwScd15dhoCUFHj0UdA0Q9zPOQfOOstw9xjnsgADOXL2QJ5YdVkDkb/kpeeo9KewtmwMB9x9ufzydnzHv/2tybuRP8NO8fqy2mXr0FHtOJFJfVoUeinlXGAuQLBHf2ekIi+ESAQUKWVV8PMZwJ/abG1ExNaYQTQwQz27FyEHRVmV4OSTDUG32eCf/4RbbzWy8rYWhwPuuqvhIOvs2S3X7Thq/9+ZMvXj2mXNq/DShT9HVXVsTo2iohySkhRcdylGtSod9JpqlJzB2K99Ckv/I5o3bMuWJjdZS70kHWWheq+R0VLJ7BPZl20vMfLH0lROpI6gzTN8hBDnCyH2AVOAj4UQnwXX9xFChLIU5QDfCiHWAD8AH0spF7TX6OYN69oUCLGIIkCa97tug10RWDSNfeUKXq/x+/V64fHHDdEPDcZHyuzZhqu7sZhPmQJz5zYt8tX3nU7C+vsbrPP7nUj7QIRQ8HmcpKZ4sCg1WJQKrKIUu6WA5OwyEuUqLM8f27JxI0c2uUkEAvReua12WZv/n5aPZxIRrfLkSikXAYuCn98D3gvT5gAwM/h5J3BUe41sDTEWBBQVBALdfLLpNgghSPR68NkaFjnp6g6LcB+EYD1xX7UV+ZNncR7fciSK9Nbg/+NIbLYqpOZDWJqpBXzXXcYgbBPuG2uZl959DlGgZRmxoRdc3YZvYtKY+Byy6+FderNH3/1I8HmxZQpsNsNVY7UaYZK/+IXRu2+MaObJdf58+Pjj1uf8SnhgNdqhA0ivG2vfIQhrZMXbhT0Rn30MNpmH+8U7ISETEHj2FqHVgGNIf1BUIxRbVRFnTTJyz8tg4KMEIevefTYraCCOOSZy402aJe6EXggRKy63qKEQM25HkwhJ8vvwOwSLFtXFxo8da1TKe+UVeP75Ol+9qsLEibB8edNi7/e3vviSsFix5A5sk/3K0GNhex4J+1+uXbf79WZ87Na+TW9zAUKiHD+tTbZ0Zzqrjxp3Qg/0eJUTmDe77kai5qcmmIfm5ZcN3/zLLxu+9qeeMto880xDf72iRBZO2RU4L/sDvvWngNdlrNADKB/ci+7ykTJ9Epk/nW2EUAYCsG0r8pFHjGVFAVUJfhYwaxZy/Hgsuf1Rstp20+m2RHtmbHfC9NGDarpuImLjnj3kHziAqqioioKqKqiKikVVUISCRVWwqKFtKoow2ihCQVUUFFVBCbok1OBnixpcDm0nWIpSCJTgvAeFw2eUJ+kBDlltLFpkiHwgYLwvWmT0yidMMHrygYAxx2jFCmMZwot9h9TkaBzr2QxCUbCNO6nBugH/G07+ebOpXLQCy+Cx5Nx9l7HhNGD86XXHhojP0ymI5ks+xgNxJ/TQ4130CHr8Q01EvP3HuSRWd+2MSIkI3oTrvQPHAAW5g5h+0QxstroQy+nTDb29/XZD4EP3h9Dn66+HnTvh88/rfveTJ8Njj4XXzPL8fJa+/TaaFsBIuSFr02cAyKoqZGUlWCzIvXuNbcuXk1NZRW8pUZzOuseK4Es2WkZKpK5T/1dY+uKLZN3xSxRrMP/8lCkNDYx2bvk4/4OJS6GP+/+1FlCEiHqP/rMflrN165bg42idqDWcg15/fXCdpuH1ayyvrOHCmWdzyZDOm3ymBjQ8aZkcddWNBHSdQCCALvXgZx1d19H1AAFdousBpK4jdWM9UqIHl6U03tF1I6FecDkkdnowOZFEIiTGdkLzHYLiuHIp/Q/mM2WK4a6p39m977663DWKUleLw2Yzeu3r1sHChXXf67rrmtbN5e/NZ2l1tbEgg/YgjcFQQKgqpKcjpEQcYcTEp1RUMHrjJrTQQQ7LI9D4c+0/xqOF34/S2bVw20FoQDieiT+hFyLu/9NaQokBH/3S554gqaK0TfuqwHHAZ4mJXDLk/zrUrvr4nYnI7D7MOj76lYrm37CSfcHR1lBnNy8PTj21ocjb7UZvvW5Gq3FTCOW4URRjW1P4/cb02t/dey+qtVF1p5tugqefPmyf0tRUCnvnAlCTlsakpXkd8I1ji2j/vdRiDsZGhqnzsRFeafV6qJpwPLfedDNA3eO9scD/Xn0Z33dfNnuMiYmRhfe1Fd3hBI+rU88RKarNeliOppC/PiTgp51m9O4b99anTzduAPXdPU2hBQIIXT9c5AEKCsLuk1FRgd3rY8fQoQScztZ8rW6BiBEPfWf+ycad0AM93kmvAHqUhd7i86IlJpGZmhp2e3FpKVZHAu5jTkQVAlURqMIYtFQVBZvFynmnnNa5Rjqc4I4NoVcsVnRABgKG+wRDsOv768OJPBjrHnsM3nkHLrgAMqs/57l7vgwrXhWAYmniz75301WZnB43AVVF2jv35hs1ot0z6mTiTuibm0jSU5ABo4eyrbBhmsFQZkxZ62IN5sAMZcsMLWNkAa3vPldEMGIkKMZg+KmFwHgP+Z8B9ACqHkDYHc1ZieZM5LYrrzKWBEhp9K10qYMukVJnb3EJenDAz/CNGz7y2nz+tRbXfS8p6z/Z1f0BCyFQhOHaUoQA1YLicbfiynYeiqIYudHr/XjD+esffPDw4JTQYK3PB0uWwDO/WMp+m51Mr+ew8ziAgU0J/ZVXwgsvhM2etnLiJJKqq3Hk727rV4xZRL06EfFK3Am9mdQMSndVIscmMG1jJ+XDlzrXv/YoaVVlzTazNPOYLxSV5LJD/Of6n3a0dRGTClSnpEft/PVpagC9sb8+1Luvn8umcUhmZVUAe4qPW/7619YZMWUKPPEE3HKLkeaynvoN69eXgs1GQrJNL7/MkVdd1cZvahIN4lDoZY+Ppf/FUf3pu62Q3iMaiZis67kEO+L1NxrLoUpY9bbr1FsvQdf8pFSV4e8zgqTxY6He4K8MPgII1cL5Z85o0sbzLr+Sb78f1rD2T7ArLhTFeLoIvkTwhVAQSmidUvud6v67676QqLeqfupqPXQdjG/MuMHhqk93PUIotdWOQtQPY28s5t9/souir15D1yX9auCec4QRconAl+BGbasroqQkbOWS9AULKHnoIfxPP0PFO++wfM3aoOGgJCUx7p57sHZT/30shSN31mhB3Al9vD+CRcLUk/oz9aTOC0v0uDw8+TKMyB3JT666pk3HmDB0KBOGxobIxgI2mw0pBO7yMhJ6ZR3Wg3/ssYb++gGWj1jt96NqRjUl+2hqbxRVws5ATWv+hE0RGhhoXIvQ76dPeTlbnE5St26Drdsa7LbvuOMYfNZZbTtnFKh/EwViQ+nNmbGtpKd36Tub2sIu5nXuKNIHDITtGylZt5aEk089rAdfUtLQX1+Z54dKuOvuu7F3ZIx6aGDgvvsazsJSVRLOPJMJZ57Z4A609U9/IvDc8wilzRnPu5zGN9F/XxKdqJv6JXNLSmBQJ6pxfAq9SScTcreYQt9R2FOM6CRfpTFTt3HETWgANuSX/3iJ0WO3JnRCAfgpUwyhX7LE6NkrCvzrX8b6Bx9scAeSO4xxoFCkUHeg8U200STeTmHePOOprLzcCIVNSzMmutUvmft/J8PUTkrqHpdCb8pP51LXoTevdGv46tUXWf2hUZ9UNu4BB8ca3vrqa+TS5Qjg97+QRiiRkCx+H5a8bzRVAK+ioKgqSmcJbOOQn9AdptEdSB8wwLCpqUieGKTxTVRVOrdHf/fdRgXF5tB148bjOTxQqkOI+H9HCKECK4D9UspZQoiLgPuAI4HJUsoVTew3A3gcY8Ljc1LKh9ptdTOYPvouwLzIbUILZZ20O0gbNrJBL1JKiV5eTlJ6LyMCB4wQ0+D0fBlMmxDKLaNLyE7r5IihxvloQuvq3QDkpk1GOG64CVhRpqmcbI3nHag/dF6HPi8PHn448vad5QFrzW34NmATkBJcXg/8BHimqR2CN4cngdOBfcByIcQHUsqNbTM3QsyOZqeiBwfpzA596+h3xCjWffQO42dfzMk/uTja5rSdejcAuX69Mc8ixnr0zYWjNp538PQl0phO3gksWtSafpFoMP7dkUT0vyOE6AecDTwA3AEgpdwU3NbcrpOB7cGSggghXgfOAzpP6M3eZqfi8/rY8I3x8NbC/71JI9KysgDYvX4NVaeeQXJqWnQN6gBkMLon4HbjrarC4nSitkP0/W433srKuhWNfmNG7rvmk6oJRWHxQnAKsFoECrB4IYwepqEHJEsWOrHrGDOxgy4T2UkPJJmZkbdVFGh2jmE7iPR/5DHgLqC1w/t9gb31lvcBYSsICyHmAHMABgT9fm3HFKDOYvGrH7Jm4YsA2DtjIDCOycjJRQqFkg1r+Oi5p/jpr+ZG26R2E4q2qb7xJqoBV3Iy4/O+b5PYBzSNdSdMw1lT0267zgXOrR9h/D/4fNUVFOROIRH489V1m1zkkKgfbPc5w7F6dfjZ+qH7Uv31w4dDZ2WYaPF/QwgxCyiSUq4UQkxv5fHDKW7YLreUch4wD2DSpEnt6pabHc32UV5Yyit3/ZqAvwZQOPmaX9Crf2+EolBdZsyGPemquxl/2nHRNbSb4XA6ufQv/+D139yBtwPELBYYcvHFbC4rR3e5sLz/PglVVeh+f5uEvqagEGdNDeWjjsR+VL3wk0YT+6Auf364bSHKSqGkRJKZCelpkuKDg1ACFYwdqFFWCqWlkJFhRMAMmja61fa2RF4evPji4SKvqvCrX8Gjj9aVh7TZYNgw8DaTebQ9RPK/MRU4VwgxEyNVRooQ4lUp5RUR7LsPqH9f7QccaL2ZkWN6btpPwY59+D2FtctfPvfnw9qMO/VYLLbYG4CLdfoNGQaKgh4rNQDbiTM9nQn33A3ACp8X2/z3w7aLpFhV1Z49AKTMmMHoOXM63NZld36CosIJvz2/VfsV7y3C6/Lg90r8Xp3ENCsWm5XsgTnN7vfKK3Vpg4SA884zisKEZjqHfgJCwLXXQlY27IuW0Esp5wJzDYPEdODOCEUeYDkwXAgxGNgPXApc1iZLW4PZo28RKSVSD1YYkjpSxyiqISU+jxeAYy+4FXSJx1WD1CXrv34PXavAltAXW7xmMewKhEDKThp1iwG2v/UWloREFJsV1W5n524rf/qLnRqvnbeesPPok04mHOPAYrejOhy1fn3Xgf0AOPs0Uzi8PbShE7j5+418+Ur49M0jJ6/jtGvDZ1jNyzPyw4U6njYb3HVX3U0uFEMPRpsJE+jUXmqbR02EEOcDTwBZwMdCiB+llGcKIfpghFHOlFJqQohbgM8wwitfkFJu6BDLTZrk2zc/Y9k7TzSxNXQXbPlHZbPbmXze9NrlU6/9CZXFFaRlx0YisO6M7KzwiiiiBid98ZcHqZ+AIRd4yoHhDwCYa/T66qMH8xopQGL/fp1inx4IoLutPH/HW8aKxkWxGq0D8HsFkEHOwEostiSqygS9+vrZucbGlh8U9m56q3YfI9urBATl5XDPeXXHSUkIsHW+ytb5RkNXOfz+orpMq65Vgu22RFRL53SgWiX0UspFwKLg5/eA98K0OQDMrLf8CfBJe4xsLT29Q1+ydx8AuSOnY7E5apN4NUj+FUwMZqQoFgiUulTFQmBzOhl3WsNxc0VRTJHvCIQ4rMhIPDDurl9TMH06mquGgNeL7veje33s2enlzdd8qNKHw+Jj5hl+eqX5kD4/us+L9PtrX2paGiPHju0U+3xqASq98HkaTzILprk+7L/EWK9ayjj1mpNJ750BgK4FeP7O9/B7rbirbYe1B7ACGSl1WywEqC5XagcQLbokI6WuvSrB7/Giq3uAU9v+JZsgtoJfO4B4/ANqK+f+8gaS0mO3VmdPRRCfRRNUi4W+YcoyjgASp7fso+9svIm7sabs47Y//LFdx1EsKtc/dmGL7fJueoVFz2xhuvyKKepyuP9+mDu3Nsg/z3s0i5RTmP7kRUyZM5YF/36UvRvXATe0y75wxJ3QA2bYTZjQLZMYQsSn66Y5wk2yjQZdOfdjypXDmfLyjYfXeAwm25mif8cUsRRWH4IHB4C3qrnDtYv4FPoeTw+/0cUw5cXF4PNStWsbuq6jdKOsjyatpKl8QZmZRmdUUcBiMUZtAwGYfBSMOqJTTIm7X5nZizWJZZxJibWfH/3pubz4x3ujaE3PouE4VRcxZYrhrmmcf0HXjYD6s84yRD4QgICO7KSsZnEn9GB6buow73qxht3h5IpHniJ7/DEAlG5cQ+HePVG2qgfRldqQl2ekdc7Lq1sXypGs68ard+9gCk0VVAUc9k4xJf5cN6a21eWJNx9vYpKcfv35v7l/YOHrr7LuvdfZu2MbOf3bm/bDpEUkdJnSN5VVrXGO5CuvNF6LFoGvCoo6JxVDXPboezy1Om8KfSzTK9eYGORzu6NsSc9Aii7s0DeubrJokbE+5Le//34jV3Jo/dy5xtTYTiL+evRmlx5zMLZ7EEoK9/1/niPvtRdRnQnc9M952B3ds8h2NNixcT0fz38PKaHMH8wpUJsHp74WSBBq17l1w5UICxHy1zfu8QfN7AziUOjN9Lkm3YPh48azctxEvDU11JQUESgvpbSoiNwBA6NtWrdh2eLFlPoCWAJarZj1z85CEkzxQcM0HxOP7aJEfE1F3IQI1+NP7bwbfNwJvemtqMO8FrGNzW7nynuNyTsfv/Qcmz+dT0DzR9mq7oXL7QJd57f3H554L+o0N3kgXI//x2WdZkrcCT3Q4z0X5gNN9yNU+zXg11po2XOprqzAVVWFYrFgUS0IVcHldqN2xwRx4Xr8ptCbtAVzMLb7EBJ6TTOFPhylRYX8819Phi2qau+uHZt6Pf7q0hIKtm/ttFPFndBL2eM79CbdEFU1/hRN1014CvbuBUUhJ8FBVlYWSB1dl+hScsSYMdE2LyLKCwvYuvRbqkoO4aqsJKNPP5xJSaz5YgGl+41CfAPGjOuUc8ed0AOm0psXoNuhWIKuG7NHH5bqygoAjp50DMee0vHZHTsTv8/Ljws+Iu/t/+H3enAkJuFISmbr0m9BSnoPHc6Jl1/DgDFHkdmvc+ZTxJ/Qm+6KeinnzWvRXQj16DW/r9l2uq5TU1VJdUUFOf36d8tcOdXLDmLrl4ytb1KD9fNfeYnSslIURUEIgRACRQgURaG0vByAxJSUMEeMXQKaxv9+92sO5e9kyNHHcMo1N5KabVSmqi4rxV1ZQa8Bgzo9UjD+hB4we7Qm3Q1rsGLXxrzvyN+4npryctxVlXirq/C7XWgeN7rXg/T7EMEb+KhZF3DW/10TTbPbxMr3v+Vrm1F/KN2qkJiQQFlFJTWh+ZuhahyNEHqAPgO7V+ipz+3iUP5OAKZddnWtyAMkpWeQlJ7RJXZELPRCCBVYAeyXUs4SQmQAbwCDgHzgYillWZj98oEqIABoUspJ7TfbJBI0M4Kj25Ac/IPft3Rx7TqpKAirHcVux+pMxJqZjSM5GSmhZP0q/F5vtMxtF9+p6wl1xio8Psq8flBUHHqA6266iazcPoDx9KIHAmiahq4HsFis2Oydkwums3Amp3Daz37OF8/9m//97k7OuvlXDDumi2L569GaHv1twCYg9Ox0D/CllPIhIcQ9weW7m9j3ZCllcdvNbB09PbxQtRj/rS//6lpARQgrQrEhVCuKakO12FEtwXebDYvNgcVmx2p3YHU4sDkc2JxO7AnGy5GYgCPRiSMpAWdKAs7kRJzJCaiWxpV6TNrK6MlT0G+7G4vVRlqvLDKyc3AmJoZtu3n1Sj5ev4rE1NQutrJjuOCi8/nfu/MBuOnnP68V9sYoioKiKFis3bsI/VGnz2TI0ZN596H7eP+RB7jkvofod8ToLrUhIqEXQvQDzgYeAO4Irj4PmB78/DJGicGmhL7LMN3ScPxFMwloGl6XC7/HjebzGi+/l4DPS0Dz4vdU4Q2UoOs+pO5H6n6Mh67WoasC38AkI5RTSqOghsQofi2NEE8RfEdKY4p38F3QcDlj/JHccuPfOvpydAsUReGo46dF1LaqrBSAxNS0TrSo8xg5bjwEhf7JZ+Zx3333RdOcLiE5sxeX/vGvPHndT3nvofu45cU3u3QGf6Q9+seAu4D6delypJQHAaSUB4UQTWXkkcBCIYQEnpFSzgvXSAgxB5gDMGBAO0eee3iPPi07nXNuu6rV+2l+P55qF+4q4+WpDr5q3HhdbrwuFz63B5/HQ1VlOXu3f4HPDqKkyrjmwfqzRvFZQBHBVNsC1QoWiwBF1LUL1asVArWghpL1Wzr0OsQrNRXlQJ27pzsyZmB/1u82Qgr37dpBv8FDo2xR52NPSCR70BAKd27nrfvv5fy7fo/V4Wh5xw6gRaEXQswCiqSUK4UQ09twjqlSygPBG8HnQojNUsrFjRsFbwDzACZNmtSufnkP1/k2Y7FaSUpPJSm9ZZfAV6u/429rX+K3Q/7MJdPOC9umqUyt4fjT7T9BBLrhDMcoUF1mDIXt35OPz+/HarWiWixYLDYsVhXVYsVis2K12rBabVhsVixWGzabrXZiVrSZedHFrP/7IwA89/J/uP2WW0jr1SvKVnU+lz/wDxa/9hIrPnyX9d98wYQzZ3XJeSPp0U8FzhVCzAQcQIoQ4lWgUAiRG+zN5wJF4XaWUh4IvhcJId4DJgOHCX1HYc4G7Ro2FRiz+Mb2H9Vkm3B5m5qsGyolaoWPvz5wHRarFSUoThabHYvNhjU4hmCzO7DbnNjsDhyOBOxWJzabHYc9gYF9hpOU0D391q2heM8uAL5fsx7WbWrdzlICDV1nIpgyMdRBEjR8QBMI412I4DpRL/wRhFCwqCozzj2PoaMi8z0nJCVz8TmzePPDjwD4+K3XufymW1r3XbohQlFqB9HTsnt32XlbFHop5VxgLkCwR3+nlPIKIcTDwFXAQ8H39xvvK4RIBBQpZVXw8xnAnzrM+qbo6aOxXcD2sh2oupXh/QY32aa5TK2NSRsxhMoVm9E2H0QGQNUFuhQ0H1XekJpsC79/Yn4r9uieWP0+JHD6tKnoAR1N0wgENAKahh7Qjc+BAFoggK4FCOgB9ECAQEBHl7oRzVL7MjI7Gu8SXerI4IxTKUPrQlkgabA+NLwSAHTVwn/efAuH/jpCgE+X6AhUZJ2XDuPG4LDZuPmuexg1cRInFx7k6x9WommtHx/qjpTs38uahR8z/sxZDJ7QdQGI7Ymjfwh4UwhxHbAHuAhACNEHeE5KORPIAd4LDjpYgNeklAvaZ3ILmB36LmGvezeZeg5WS9M/oSlTjNoK77wDF1zQTG8e+MXNjzRYllLi07y43NW4PNV4PDW4PTV4PC68XhebNrvYttXNgAFesrI9rHv/Q6x6bLglOhufqwYhBFNPnxFtUwAjDPLZfzxMjcuNLhR0KbEK8AJOiwVFiNobhkcL4PYHeOaRhymrcaEFJ4pNnHJ8dL9EF6DrAXasMBKXDR4/sUvP3Sqhl1IuwoiuQUpZAhw2FznoqpkZ/LwTOKq9RprEHoX6fgapw5ptE6qD7PPBkiUwdmzzYl8fIQR2qwO71UF6SkPfbV4e/OKGhr7/zeoClOSeUbDD53ahxoivHYyIoRvujCzg7se87/jwk08prgkgFeM7jB86iFFHd63wRYPv3/wvy957E4D0Pn279NxxNzPW7NB3PjUeFxXWYvo7m8850ioffSPy8lpXs8Hq0rEM7V7T49uK3+tFtdqibUabGD9lKuOnTI22GVEhZ0hdx2jXquWkzwwfxNAZdL9EGRFguug7l427tyGFZFjmkGbbhXz0qtqyj74+oWid3/3OeM/La7htzx4jW60QxrGPmVKCTVNITE9v83fqTmh+H1Z714TlmXQcwycfz3WPPwuAp6amS88df0JvRt10Ohv3GxE3R+aOaLZd/TrIodDKvDx48MGG4t2Ypuoq5+UZN4unnwa/3/iv1nV4453tAKRmdl5x5VhC1zRszp7hpoo3bAkJWKw29m5Y26URgnHnugHMQPpOZkeJIayjB4+sXdeUq6V+NbVI4+qbitZ55RVjXX00DfKW5nPEEKiozOyorxiz6LqOlBJ7YlLLjU1ijoSUVAYfPYlty77H53ZhTwif5qKjiUuhF6bSdyq7q3eTEsgkJcEQm0gF/JVXwOMxeuLN+eybqqtcUBDeHoe9CgD3Z2/zh9XvIlQFoaoIq4pisdDnyFFce/lv2/29YwFXhTFZypmc3EJLk1hk56rlbFv2PUdOO7nLRB7i0HVjem46nwPaPnKoS0TVlKslRF4e3HQTPP983f+Pqjbvs58yBebObXgj6N3E/JJV31/NXrsDv6qj+WzgFchqjUCRG+vOCvZ/lVdnSEt+oxinPHi3S+imeW56MtWlJaz5/BMATp/TtZPD4rJHb3boO49AIECxepBRtrG165qbGDVvHtxyi+FiCYm8EHDttZFF4NR3CU2YYNwgAo3m1khpYXfV28z7zLDBYglO5AnA7ZfPJsEpWpePIYapKCoEoPTAvihbYhIpeiDAVy8+w9ovFyB1nbGnnonV1rXpluNO6KWUps53IrsLD6CpPn5wfcd5z12CUySQYknhzffvYd2K3Aaulrw8uPlmQ+TrI6Uh2vUJ5+Ovr80h8ZbS+HzKKbBwYcNjhJ4qdL3uPEm6nxolvX2xnjGEUI2H8INbN1Owcxu9hwyPskUmLbH2iwWs+fwTjjp9JhPPPo/03K6NoYc4FPqAX0e1xp1HKmbIzcziOE6hWDmER7rYad0IwCn2k5k7d3aDtosW1YlufYSAkpK65aY62/W1ub54h8JnFcVYryiGW8diMZZVNfT0oJHoB0tZCb91fY9y+clY8CGQKKUrOeKTZ7hg5g0dfIU6l1EnTGfFB+9waPcuM8SyGyClZPWCD+k9dDin/eznUbMj7hRR8+tYrLEzazDecNodPHvV47z3s9f49Pr5zB1kpC7ql1Hnsw+5wjMzwW4/fF6DEMa2EE35+EMuIUUxXqpaFz8/fjxYrcZnqxVSUurcQ5pmHEtKCzszhlCgJFPlt1FDIl6RjF9JxrrPzYYvGj0SdBNswUG81KycFlqaRBNXZQWfPPF3Sg/sY8KMc6JqS/z16H06qi3u7l8xy/6KAwAMyjFqCDTunT/2GKxeDS++aMS+67ohxrffXpcSobGPPzPTuFFMn27sH/LxC1Hne3/00bpjaRr8/e+HPz3oOjzz9BOHDdAPGfMJPx/1bzRrQidfnc7BW21EGVls3XN2bE/AXVXJW/ffS9mBfRz3k0s4ctrJUbUnroReDxhZ+Cym66bLKKguQNUt9AlOVmrcOy8pgaeegiuvhPvugy++MAS4vpu8fjhlZmZdfhybDY45xrhBQMOIqtA6OHxwtj7horB+PurfALi83fPJz+uqQVHj6k837vj4nw9TdnA/5999HwPHjY+2OfEl9Jrf6NKZPvquo8hbSLKWjqIY17ypCJwpUwyhX7IkfHROSPAffLDuRuH1wuJWVy4wlP04ljKdr/mQWWxgXO3Wi39ya+3n3IPF/PbG2QSE4RcKoOJIsJCanczNtz6Cwx6bPX6fx4PazeuoxjOuygp2r13NcRdcGhMiD3Em9IGg0Js++q6jzLOfKkcJM14YiwBU4JQnAB1URfD3TaBsMgpXKAjO/adAD4DVInhmi+DZLQIlWNhCRUHLhvP/KnAtncGn79zXanuG/+Vc7L3z0fw6H9kVBP9jDHDotv9SWDGOjbtPYPJQo3BHvt2B6taxEMCCH6uU2Mp02F3Ott3rGTticgdeqY5D8/mwJ8TmTciE2ipeQsROhzOuhD7Uo7eYPvouQeo6o/cfT+/sH8jpVY0uJQF0JEbucb3eu5SSABKp6OhW0NHRMQpZBDQ/gYCGtKjoisLmXhqjJ32OfPu+VtmTMHwF9j75ADi9Oh573e8g6/HLsS6dwZ6Vp3Dnmx81eYxZ593DdPt6+ufGbg1TI9eNKfSxyPblS/n6ZSNxWXJm7JRGjC+h9xnOWtNH3zW4Cg4yoHgKlw8dyrjLLm3bQRqN3uqff8akbTchSlufoCz9pHeN9wqNxbdtBuDD3HH85kGjA5B23ALSjltA/t+T8JXkEqhJRXcnI/12QrPsUh0leC06acmxmTfHyHWj4zBz3cQc1aUlfPDIX0ju1Ytzf/Ubhh0TO/M0IhZ6IYQKrAD2SylnCSEygDeAQUA+cLGUsizMfjOAxzGe6p+TUj7UAXaHRTNdN11K6WYji2XGoHaE+S1ahNvi54XbcihKtVK15uf4EwXuwv6tPlTlqlNIP+FDJmyvQWJI96yDa5l1NawemMKcm0fjza5i0J0N45lrB2ylQubKTNzurp212BrcFRUAOJN7Ru797oQ9IRF7QgIZffszfHJsVcxqTY/+NmATEPqF3QN8KaV8SAhxT3C5QZmZ4M3hSeB0YB+wXAjxgZRyY7stD0PAHIztUkp2FQLZZB45ssW2TTJ9OpsWJPP0ERkA9PMLhrpUNi29sNWHcucbhcpLlVRgL1CXDWPC7kp+uGspL5+VybY+dhbYT6bUnoSwelHsHoTFh2Lzkujx4k+M3QfdsgIjnNXMdRN7WOx2nKlpVBUfirYphxHRL1oI0Q84G3gAuCO4+jxgevDzyxglBhvXE5sMbA+WFEQI8Xpwv04R+roevSn0XUHBbhcAoj2hflOm4LvzTij9H1e4BvDHX32M1xt+Rm1LaGXZSAk7UjNre/QhjOLUkms+LUYC14tvef2GRWQeMZCSEiOss6QEpPdsxMDY8a02JpTrJjGjZxRZ6U7s37SBsgP7OPW66M2AbYpI/0IfA+4C6udGzZFSHgSQUh4UQoRzqvYl1LUy2AccG+4EQog5wByAAQMGRGhWQ0I+enPCVEM2bC3h5YXbmTg0k2lH92bx6gKOGp7ByMFptWGRbaGw1Hi4CzROEt9KVknDn75KKcTnqxN5W59tOAdswVs4AM+usbSYrU6qoCskJx9stqVQVQb/ey5z5wxssL7GXcnTVwusmRlt/zKdTFVxEQApPaTISneiuszI65E7rPmCPNGgRaEXQswCiqSUK4UQ01t5/HB/b2ETCUsp5wHzACZNmtSmZMNmeOXhbN5VxtkvLAXgzX3F8M0WY8MiOMJiY8GfT2/TcT3Fh/AEEhiZu4vEvqe0y8brZvybp/43hY0OL0c+P7bJdrrXge5zEKhORavMRKgaWlUGWmUaqrMGNaUUoeoczLQi4LBePQCTJxvTbcMkNJv3wu8ByMzuc9i2WKG61BCTlGxT6GMJv9fDtmXfA0YcfawRSY9+KnCuEGIm4ABShBCvAoVCiNxgbz4XKAqz7z6g/qhaP+BAe41uCtN1A2UVHp6evxmBxGmz8OKPe5vsCG/WfJz9hy+wqQpWRTBrfF+unBVZb2T9m5+hyT5MmH10u22225IYLi1sE9ph26RmwV+eBbqC4nAhbB5svXdjz93d5PHO/CHMH5oQdTkZGon89j0bWLd5KZXbdpMEzDrzmnZ+o47nrfvv5cDWTQT8xjVKy86NskUm9XnvoT+yd+M6+h4xmtzh7Riz6iRaFHop5VxgLkCwR3+nlPIKIcTDwFXAQ8H398PsvhwYLoQYDOwHLgUu6xDLw6AHJIrVRXVVEaj1smlJkDLUxyNYq1Ei9YCxDT24rCPRg9v14H46UhouoWKXBa+WgabrBAISTdfRNElAl2gBPfguCeg6+bth5y7JwIGS3D4SXTdiygN63Wddp3adX9O57pyRZKS2LyPhfz7dzjOb9tetEPDTQVk8eONk9hdWk+S08pN/LGGHx8sA1Uq5FsDv0zikB3Cv3BuR0Gs11azdkMTA9Hwyj2pfbz7EY6c8wdlf3wTASxN/w8gR57DsB3j7v0m1laV69zbSG//i5xp64kEsaYeQmh1h9YBU0MpymFi6jYfkKXW9eUWBO++EtLTD6xwG+e/f7yWpUCMJcPV3kprcsa4bf1UZn/zuGvz+ABaLiqIIMnN70/uIo7A4nFgcSVic9V4JKVgSkrE4U1CD+Wz2bVqPHgiQkJpGYnoGqTlNVGEx6TJ8bhdlBw+QkpVNZXER/Y4cwyX3dVpQYbtoT3jBQ8CbQojrgD3ARQBCiD4YYZQzpZSaEOIW4DOM8MoXpJQb2mt0U3j07xhx/t1s2RO0qAPZXdmPPy29C9jRuh13RL5L7zQn/xdhj7oxmqbz/qJ8/rl6Nwj46pYT8Ph0Eh0WBvYxhlb65hix11/ed1qDfX3+AEf+dgEDUiIrOL35jXdxB/ox4ayOE8RrvrgRVME43cKQnAkk2ZI49QQ49YTD244da+GVV/pTUNC/VvxXrzZKDfbu3Yf8lKcYuuh56NMH7rqrxbzzaoUPUPDk2Lnpnn922HcKUbjyc7Yf1MhM0FCEn0M1FrYd2Asr97a4r0BiVXR0XUEBHP5ilJIS3vr5OVgsCpPOvZCBZ1zV4TabNI/f5+XFO26qdaWBMVDuddV0aYnASGmV0EspF2FE1yClLAFODdPmADCz3vInwCftMTJS0vvWULoLEsUcFJwUl0BREWRnQ69gIEVtylyhUHxIUFAo6J2jkJUljCnLAgQKoFBYKNi/X9C3r0BRjSkCl4yUjMwcRuFBwWv/VdD8AlUR/OxnCkMGg0VRePMNweJvBFIKpK5w8ikw52eKkWpXEShK6LOCogp++cpKtvp8LNp6iFX/LifZoZJkt5DksJLstJCaaGPMkHSG9E8N+70//mY3N3+6HjD+Q49OTGBIv/Btw7FpZxkBASNyW65DKmtK+XGVSnZyEX2mXRLxOVoiIOAcSy/+cvnXLbatX3A8PLXj+hGRdtxoKr5fj+7x0atXx7tEyvI3ATD7nj+SNvJYPMX7qN6/Dc1dheauMV6eGjSPG83nQfMaL7/Pi+bzofn8FBbXYLdILELHr+loWoD8Q5LMpV+bQh8F/G43NeWGJkw65ycc2LKJrIGDsDki6yx1NbEbMNwGFNVwsUw84SZWrEjhwiubrhyXlwcX/F/k2//95g/AIaaMdjB78hE8+CBs+t5IvqWq4J4F5wZdux/+D3bVe25xTIcpjSoq1eeRy4/mpheXk1dQjlcYoteYJClY/9eZDdZVVHl58u2NzNtiDHvcPq4/180+guSE1qWvXbu9FIAxg1oO2dv14QdUaAM444wkRONE8+2gN1ZKtZoOO15ruOWmh7lv9dlY0zonrUDZ/j2oQidlyHgAHL364ejVr13HdBXk89Rtt5CcmdUBFpq0loTUNGb8/Jd8+q9HEELw0/sfjrZJzRJXQi+lkbtWUSwtVo5r7fZtmw3x9PiMczRXJ/XKK+GFF4xUularsdwcY0dm8u1DM9ADOppfp6bGT3m1j4oaHxXVPv704UbyfT5m//ErHKqCXVXQkHxXUV17DCHhtp+ObZP4btpnDF6OH9nCtH89wI/L/CTbKhh6yvTm27aSHEsie/yVHXrMSAnoAZzVEsuwzgmrLDtUQppTR7F23Izbqt3GE1xKbttCkU3ajt/r4Yvn/s3Wpd8BdItKX3Em9EaPXghLs0IMzQt1uO3jx1j57yLwBYyoh/o51BuP8YXK4IXb1hyKqmBTFWwOC+mZdY+A5+ws5Z11Bzjg9eGTEl+9urjZDitzTx5Bv0xnm3vYO0pqSJWCzPTmHzsLl3zBQfdQTjjBhaJ2bGRTb3s6P2iHZdDodBbv+IoPFrxAbkAhOSd8yKK3pgahKlhsNhQlstBdXdNQLMafV1mFh/R2DrI3pnJfaOCnTZHIJu0gf80qNi7+isHjJzJx1vkMGHNUtE1qkbgSer2e0DcnxNC8UIe2P/YYvPMOXHABjB9nh0Xg0wIN2jQl4i37kSPn9kvHcvulTceXt5e9NV762Ft29/y4MB+b0pcjzz9saKbd9E7Iptqdz09fHI9NqFiFil2oxmfFgl1YsClWbKo1+G7HptqC73ZsqgObxYHN6sBmcWK3OLFaErBZE7DbErFZE7DakrBbk7DZkrDZkrHZEnjhuT8wdqcxnjF0RMM/WOn38Y8rftJgnSJ0LIrEooBFkaiKwKIaYy+qKrCoCruLDfHtm6aTnplKmVthyNCOe1rY8sbDfPTuNwB88MZCrh4xkcwxUzvs+CbNk9HHiBgfdswUBo4dH11jIiSuhN5w3YjaPNAtie26dXVVjRq3y8urq3S0ZAm8OcwQQq92eKx3dyYQ0DkQ0OjlsLJtdzlJTivJiVYSnJYGs2Yrt21iR8kQxh9xCFtixw84nTruGtYv3ooLPz6p4ZUaVboPn9TxIfEi8QvwAV4B/g4aHzilOovKBD8X/9+12AsPUrJuMRmjT0AoCksfu6223bRj+hIIBNB8fjRNQ/NrBLQAWiCApukEAsbL59cwAsyg2q1TsaeUBItk4MQw4UMRsu2dx1jz5WfGuXVBYU2dC2hwrr3W92/SueiBAPlrVrFs/lsApPWO3Yl1jYkzoQ8gRGRfad48uOEG4/PCYI3oOfUCNRr76FctN/64fFobkrDEMBVVRvqCtTVuTn/qu9r1QsJpbisz7QexWXVq3FYE2Yy74MROsWPAgBN45IolEbeXgQB+fzVebxU+X7Xx2V+Dz1eDz+/C5w++a258fje+gAef5sGrefAHvPgCXrwBLzXVh8BlZcEz/wke+VPgbw3OdfJJozj65387zIau4sevvqCgTJKTmUKCRTA8TWfo0ccy+op7omZTT6OmvIw3//QbSvfvJSE1jVOvvYkBY8a1vGOMEIdCH5kP9Z13Gi4//3xDoW/so586xcYLnzR03cQDGWkOnp41lvzCalxeDZc3QFlNDe/sq8Bh3Q8oVLut+ANWxo88SFL/M6JtMmDkq7GpqdgckYeRhuOtz2exx8jNxsnTR4OUbFu7kX1lYFc1xh2Zw5grGufq61oqqzUG90lk1iPvtNzYpFPYuWo5pfv3kjVgEJfe/3DMhlE2RVwJPVKPuHzXBRfU9eTBmHCTl1fnwmnsw58w0QFxKPQAM05oGLmxavn3vLMPzpvRm9NOPytKVnUNF/7rAwI+D4pqQbEa7rn2J3XoOKSuU+0VVO138/QVM4yxARUswfEAi1UhwWnnlLsfa3fIpknTHDH1RLYvz2PnquU8cdVF3PzC692q+EtcJYUxom7qvlJenlFsOi+vYbu8PCMl7Yn1vBC6boh6U9gsQddNIL5cN+HI32/E5Q8e0PriH90NoShYHAm1Ih9rCEXhlDOP5agjshgyMJ0+vZPJSHOS4LSiKILKKj+b9ngo27Ii2qbGNVa7g9m//h3jTpsBwGdPPR5li1pHXPXoJXWum0YV6monRNVfr6pgt4OmHR5iefj+Chah4QvEfzhbflEFCnb6D4zduqk9ibHX3Bd2ff6bD/Dx/MUAJOYO6UKLeh5SSj57+p9sWPwlAELpuMmCXUF8Cb2UtULf1ISo+usBrr8eBgw4PMQy3P4WRYu7wdhw5Jf76auWdTs/ZE/jqw+/xBMwZvMm9ou9HOjxRHVpCRu++YJB4ycy6ezz6TdqdLRNahVx5bpBBgjl5A0Npqpqw956/fWqWreucXhluP0tSgB/T+jR11gZ5HBF2wyTZpBSUq05mDgikVtffAPVFvuzM7szpfv3ATDu1DMZOG48qsUaZYtaR1wJff2om9Bg6v33N8xjE1p//fVGgrNnnzVcNI39+OH2typ63Au9lJJd3hQGpXSvR9Oehq+iBL+uUFBUScnqz6NtTlyiBwL43C40v5/1iz7HancwcFwzSatimPhy3dAw6qapCVMhF46mNZ3rJtz+FjWAL/6CbhpwqKiQKhIYlOGOtikmzaBYrWQ4NfaXq6x49z+cM3V2tE2KC6SU7Fy1nJUfz+fg1s1o/roymUefdW63dWfGldAj9WCK4ZZpKddNOKyKzvqDDq5/9pnavDLCyGtsnFUIatcKUBWwKioWi4JVtWBVVZw2lUSblQS7lUSblSSHnQS7nSSHkyRHAsmORJKdySTaE1A7OJ9MJHy43Cg1mB9oIcGZSVSxJqZyzUsLePryM7E7OyfrZk9CSsnudT/y/RuvcnD7FlKzcxh3+lkkZWSiaxqJ6RkcecL0aJvZZuJK6KXUIcIJUy3lugnHtCE6X29XWLk/FRmsSmpUrgqev9YOkAh0KQjoKppUCOgqssFNSGJM6PcBVWHPZ1N92FU/DosfpyWAw6LjtOo4rJBg0Un0l5GjppFos5BgtZDosJJgt5HgsBsvp5OEBCeJCYkkJCSRkJSEMyG52RuIp6oUsHHW2O4zvbunIt0VeAIqjpT2TRrr6ezbtJ7v3niVfZvWk5yZxelzbmX0SaeiWuJHHiMpDu4AFgP2YPu3pZR/EEIcBTwNJAH5wOVSysPyzAoh8jGULABoUspJHWZ9I4zwysh7wa1NPPbXy9pW4EFKiZQ+fH4PLm8NVR4XNR4P1V43NR4PNV4v1V4fLq+Xaq8fl9ePy6fh8gVw+XXcPonLL/D4we1XKHOrePwWPIF++H023IQbiPMGX+WHbXHgJUH4SVB8JCoaTiVAoiWAU4X9biuCLCaOir26lyYNWfqPWwlIBUeyKfRt4eD2LXz3xqvsXruaxLR0TrnmBsaeOgOLtXsNtEZCJLcsL3CKlLJaCGEFvhVCfAo8gVE/9hshxLXAr4HfNXGMk6WUxR1jctNIqTeIo1+0CMrLjXeHA0aNMnLDh8Q91KY1qYTbghACIew47HYc9lQyUtp/zPxFl7NDX8pJU5aiWFJxu6pw1VTjclXjcrlwud3UbNmCa+cuXLm5uFIzcHn91HgDuP0Bavw6br+kRhO4NIEroFDiU3HrFs5K22+6A7oB+XuKAYWRZ1wUbVO6FUX5O/n+rf+yY8UyHMkpnHjFtYw/Y2a3yCvfViIpDi6BUIULa/AlgZEYPX2AzzHqwjYl9F2D1AGldrKTx2O4UUIsXgwvvghfB6vVhZtQ1V1we/djBSxOo8JQYmomian1/Op5eXDjbd33C5q0SEWNzpjByaQOHR9tU7oFXlcNi199kbVfLsCekMjUi6/g6JnnYusBnZqInFDC6CavBIYBT0oplwkh1gPnAu9jFAZvar68BBYKISTwjJRyXvvNbupEhuvm668NfZNhIiFDETahz81F3cQyHq0Up2imYlHjGV+vvNI1jy8mXYK/ppIav4XUrF7RNqVbcHDbFuY/fD+uygqOnnkeUy74KY6k7pOrpr1EJPTSSCIzXgiRBrwnhBgDXAv8Uwjxe+ADjFHFcEyVUh4QQmQDnwshNkspFzduJISoreg8YEDbyqOFXDehiBqv18hhU5/6ETatjbqJJdyKi2TRTL3QzExQFONupyjGhAEpjZwPZu8+5nEX7eGLv96O1CVWqwWrzYLFZsNqt2G12fH7jD+3tFwzkVlL7Fm/lvl/+xOJaen85J77yBkyLNomdTmtGlaWUpYLIRYBM6SUfwfOABBCjADObmKfA8H3IiHEe8Bk6lw+9dvNA+YBTJo0qW2zkoLhlfUjaprz0bc26iZWkH43HqtOttI7fINQ1ZRAwJgVFgjU3fG83u73+NID2bfkPbbu85Hm0NAl+APg1xU0vWHkVtaoY6NmYyzjqammdP9eCndu55tXXyAtJ5cLf/tnktI7py5wrBNJ1E0W4A+KvBM4DfirECI7KN4K8FuMCJzG+yYCipSyKvj5DOBPHfsV6pAEIMLqUpG2iUU8xauQiiAhsYlEViG3ja4bQl/fhyVE93t86YGU7dsJwBWPvYQ9ve6GLqVE87jw15QjhIIzMzdaJsYcUkrWfbWQ5e+/TXnhwdr1/UaN4dw7foMzuQOiILopkfToc4GXg356BXhTSvmREOI2IcTNwTbvAi8CCCH6AM9JKWcCORiuntC5XpNSLujoLxFC6pFXmOrOuEtWAeBMHRW+Qf3ZYFCXwQ3gnHO6592th1FeWEiCRWsg8mBEcFmdiVidiVGyrGPxezxs+OZLKouLSO/Tl6ETjyWhDfMCfG4Xnz31OFuXfUfu8JGMOeUMsgYMIikjk6yBg2snOPZUIom6WQscluBBSvk4cFhS5qCrZmbw806gy0qk67oXRYnNvOIdiatiMwDOXhPrVjaOFQ35pTIz4dZbwe8HqxXuuisaJpu0krLSKtKS4ioVVQOklGz+dhGLX3uJ6tISFNWCHtCwOZ3BaJjzmt2/sriI3et+pKKwANVqpWD7VnauWs4JP72KY875CYoa2cTJnkJcdX916cOixv/jmdudjyIl9vQxxoqmku+Heu5jx3bPwYgeTHl1gIF94++37Pd62Lr0O1Yv+IjCndvIGTKMs2+7i74jjqRo9y6+ff0Vvn75WbIGDaH/qLEN9i0vLGDbsu/Yuuw7CrZvBYzCLDI4/nTktJM5drY5pyAc8SX0uhdFaSbkME5w+wtxSgtCDf73NZV8P0R3HYzoofiryqj2W0jLzo62KR2Gq6Kcxa+9xObvviHg95Oe25czb7yN0SedilCMJ5ecwUM591e/4flfXM/iV1/grFvuxO/1ULwnnzVffMrBrcaTbM6QYUy77GqGTjyW9Nw+BAIanuoqkjPMUNOmiDOh9/UM1w1VOKkXA9yWDG0mMUv59pUApPUbGGVLOgZPdTUv//oWvDXVjDn5DEZOOYF+o8aG9ZtbbXZOvOxqPv33o7z4yxtq16fl5HLi5dcw4rippGY3HLdQVBVrRvx38NpDXAm9T/NQXFnM4vX1x3sFoZySkvpRmxJdlxSUrMXn3YOq2FHUBBTFiaomYLUkkuRIxm5LxK/bsVmSyEpJxWFPwm5NJCUhBYfV1uWDPFLXcVv8ZFYnGAVxG/vkTfdMt6d85wYA0gd1rypGTVG4azuuinLO+eU9jDjuhBbbjzrxFLIHDaFg53bsCQmk9Mome9CQ2p6/SeuJK6EvqNTp5fgB3fNDxPtkATUiAQUdO14UKUHDeHmMRD8YH9lb0HBfv27BF7Cj6XYCOAjgQJfGu815NJecPLdjvlg9fMVr0FWB85st8K+l4X3yJt2asn27AFj88r9xvPEcY0+fyaCzfhZlq9rO/s0bQAj6jx4X8T69Bgyi14BBnWdUDyOuhP5fq24jV3jIX9wHqUssFrj1VglInnxSoGmgqoJbboFBg4x9UhPTGT3QSKipBXQ8PjdubzWV7kpKq8vx+GqwKl48vmqq3JVomgtdd6MFavDrLjTdha7XIHUPQrpRhIf+iatAruLNz1ZwxJBLGTe84waIXIXfA+Dc7um++RtMmqXv+Kn027QNj09nb4kXy6KFMSv0Ac3Pyo/fJ//HlWh+H4pqQbWoKKoFxWIha8Bgti79lpRe2T06jj3axI3Q5+XB7kNpbNzUh9LPx9SuHz/IKP697DNDF1UVdk+Dy2YefgyLqpDkTCTJmUhWWg5D22jLl18Ze2Zaf+TQ3h+hjUJfs/cL1q6/Cb+io0qBKhV0dLBDwnYOL4hrEhf0PekiLjnpIgKeGh6/6mJSMmN3Nuf7f3+AXatXkDNkOI6kJHRNQ/Nr6B4PmtfLrlUrkFLnqNPD/MGZdBlxI/SLFgFChk1k1pVjlfPev4yhyXXLle7hbTqOr2wbKzfeAIokm0EEpJeA9KHjJ01Lx/HCK/DNYtMnH8dU7lqHRJDWO3bz2ezfvJE+I47kp/c/HHa73+PB63b12NQDsULcCP306fDUG3UDryEmTOi6scolH81jaPIyAIb/3MuA7QVgOwhf5kV8UqnrlKz7B9sPzEOzSSatm0bKsTeG3//4qcZ7VyXWN+kSStZ/h6/iEIc2G9E3qf3a+mzZOfg9Hj5/7kny16zC53bhddU02dbqcGB1xG+e9+5C3Aj9lCmQ/Ikkoz+UibqkjSUldds7SwMXr/uYg7teoFfSj8aKJ1MZsG2NkWsmQh+6+9BK9q37EyWeTdQ4AjgljHumjJR3/wu2t+Cxx4wv01jMm5osZdItqSnI5+X7/xIsVWmQNvywielRo7K4iK9fepbty/MYfdKp9Oo/MKJIGpPoEjdCD6CocNxxsPX1rgsp1/UA/kO/oFcS+AMWauZncsH8ZcZdRohaIyo2zKP40JcMOvbfqE6jQIgMBChc+iv2VHxKtd2PBJxCYaT9DPos643y7v3GwILXC7fcYtw4Got5S5OlTLoVZdtWIRGcMHkgGYNG4EjtRVL/I6JtFmAU7njl17fiddVw1Blnc9p1N0XbJJMIiSuhR0Jubp2b5vhpAbRepSzZBuP6pZHq7IxakJJt5cMYnrYdq6phPyRZMmMsuy+eQsB6gCFKOYlFP6PM7QEgP28yVp9OL08KmlVwyFlFglDpX9mX/jvG45h6mSHUSh7Y/mqId/1Uw43F3JwsFVdU7NkGwPAzLiJj7PToGtMIq92BIykJRVU59dobo22OSSuIL6HHmB41ZQqkDKzg1tdWs7PY8B9aFMFVxw/i12eOxGFte8KjgC5RBLUTpRTFwg3nL+CjJb8kQfsQ541F+IBcFgKg6RJfZYDQpbZ5dHwOhYO2ahRdMsx+GgPsVyIuOAN8eWB7vq7HXj8x2e23hxdzc7JUXFFRsBeAlMFdlgswYhRVZfCESfz42cfkr1nF4PETW97JJCaIK6Gv9mn874c9VHv9zP/xAOkJVp66/GhSnFY+XHOA57/dxeo9Zcy7chK9klo3ZXp/uZv7P9zIl5sLsakKM8bk8sfzRpNktyCE4JwTH6O48td8u/Y1CsoPMfmIcyha+DQnFy5A+UsFVs1IFeweZiXvKSOK4pgjniWp36lw0011BW7r99gjTUxW341Tf9mk21FZXEyS1Y8lKT3apoRlwOij+PGzj1EtcSUdcU9c/W9lJtoprvby2YZCzhrTm9/PGkVmUNCnDuvFSSOyuP2NHznpb18zqk8Kg3slctmxAxnfP632GFJK8naW8LcFWzhU5WV/uZu0BMPl49d0Lj92IB5/gNeX78Xt1/j35XW9ml4pfZl9wq9rl9e9ehf7fFkM1Upr1zm3+znl9F2GO+aGt2HCDnj++briIKoa3v3S3GiyOSAbN1SUV5OSEDtT/f1eDz63m4SUVCSS0gP7om2SSRuIK6F/44bj8Ad0RuYkh81Bc9bYXAZmJvLaD7vZWljNgvUFvLliH1OGZNI/w8nBCg9r9pZT6dHol+5kwoA09pe7KXf5OXN0Dr86YyQjcowg+X1lblbuLmvSFvndd/TXd7O1PDf8xCsp4ZlnjEHb+oVBZs5svUibA7JxQ0W1Rr/esVG02lVZwQu3zcHrqkFRLVhsVnxuN/2OHEPfI+IjD09PIa6EfmhWy38go/qk8OfZRp7raq/Gs4t38uXmQr7ZWk16go1ZR/VhbN9UzhvfhwSbhccvlfg0HaetoV9/dN8Uvt9RzP9+2MPRA9JJdVpJcVpwWlWEEBz4egF9lRo4qIczw0DKhiIfjkhi5M0B2bgg4PVQ7VNJ7ZUZbVMAcFdW4HXV4EhOYezJp+PzeBg49iiGTTrOTDDWzYikZqwDo5i3Pdj+bSnlH4QQR2HUiU0C8oHLpZSVYfafgVGJSsUoMfhQx5nfPpLsFn55+gh+efqIJtuoijhM5AGunDKIxVuLmfvuugbrk+0Wjh6YzvF2GzdokL6z6V5/LYpSV7z7008NcZ8ypXmXTOMbwGOPwTvvwAUXmL35GMVfVcY3f7uFgKZhtdux2e1YHU6szgRszgQ0nxeJICWn4+rAuirK2b95I163i+xBQ8ge1ESd4TBk9O3PkKOPYeeq5exZv5Zpl13FwLHjO8w2k64jkh69FzhFSlkthLAC3wohPgWeAO6UUn4jhLgW+DXwu/o7BuvMPgmcDuwDlgshPpBSbuzQbxEF+qY5+eQXJ7DhQCX5JTVUujUqPX72lbn4evMhJlTvBAsM9BS0fLCRI2HzZqOHr2l1rpemXDKNbwCPPVYXlbNkiTFwa4p9zHHgu/dYs7UCp0VDl+ALqA0mRoXoNXx8h5yvoqiQV+66FZ/bVbvumPMu5MTLro5ofyEE5/36t2xasojv3nyVt//8W06+eg5Hn3Vuh9hn0nVEUjNWAtXBRWvwJYGRGD19gM+Bz2gk9MBkYHuwdixCiNeB84BuL/Rg/CGM6ZvKmL4NixnrumTVCjvV36wk6dK98KQfSvXQThyWkCc0sUrTGrpeMjONbYrScH3jG8A775g++m5Axd7tAFzxwD9IGTQKqesE3NX4qkrxV5Xiry4HRZA59qR2nUdKScH2rXzz6vPomsYF995PalY2y+a/xfL336aq+BBWux1HcgrjTp1BWk7vJo+lKCqjTzqV3kNH8NKvbmL3uh9Noe+GROSjD/bMVwLDgCellMuEEOuBc4H3gYuA/mF27Qvsrbe8Dzi2iXPMAeYADBgwIFL7YxJFEUyaPBWOXAiPjIQJDvjK3TCyBgx3jZRGb95igeuvhyuvNER63jxjNmwgYGx77LGmJ0ldcIHRkw/nozfz4MQMFYUHUIROUn/DVSgUBUtiCpbEFOg9qEPOsfbLBfww/y0qigqxWG2cdv3NDBpnpFA49ZobOZS/i+0/5GFPTMRdVcm6LxZw0e//0qJLZ8VH72Gx2Tnpims7xE6TriUioZdSBoDxQog04D0hxBjgWuCfQojfAx8AvjC7hiu/FCa/JEgp5wHzACZNmhS2TbcjuTdkj4YzysDRGz5dXOeLv/562LkTvvjCWBcIGPmUQ66Zm282evhgbAsl7YHwk6TCxdmbYZcxRUVJKSkOUNTOiYHY/P1iPn/2SXIGD+XYn1zCiGOnYk9IrN1udTj4v78+XrtcXljAm3+cy7sP3ccVDz52WIbJyuJD+D0eMvr2I3/tKuyJiWZd1m5Kq35xUspyIcQiYIaU8u/AGQBCiBHA2WF22UfDnn4/4EDbTO2mzHwYXr0ApnphhwO2eQ3RnRBMVGWxGELe2DWj14vWCRdb3ziuPlycvRl2GVOUVXhIS0lsuWEbKNy5nY8f/xt9Ro7iwnv/hNXecsbItJzenH/PH3jtN3fw2dOP85N77kMIgR4I8OmT/2Dzd98AkDNkODmDh7JjxTJe+92dXPTbP5OQmtYp38Okc4gk6iYL8AdF3gmcBvxVCJEtpSwSQijAbzEicBqzHBguhBgM7AcuBS7rOPO7AYOmwnUL4d3r4ZJSUM+EnBl1g6eNXTZgiLrdbiQzUxT417/aJtBm2GXMIH0eSl0q/YZ0fI+4oqiQ/957BwCnX39zRCIfIqVXNkJVyf9xJaX795LZbwAbF3/F5u++4ZhzLyC5VxbL3nuTwp1GDp7iPflUFh8yhb6bEUmPPhd4OeinV4A3pZQfCSFuE0LcHGzzLvAigBCiD0YY5UwppSaEuAVjoFYFXpBSbuj4rxHj5I6Dn30B794AWz6GIktdTxvqXDYhIslfE4nv3cyDEzNU7VqDJlUy+3Xs+NOe9Wt4769/wmKzc8HcP9Kr/8CI93VXVfLf3/wSv8fN8RddTkbf/pQXFrB+0efYnAlMu+xqhBCMPulU8tesorzgIL36D6T30LYV0zGJHpFE3awFDkuILaV8HCM+vvH6A8DMesufAJ+0z8w4wJ4MF70Eb1wO2z6HVBtUBHvamZnw4IMNxbijUh6YRcNjgtJtPwKQMfjIDj3upm+/QfN5uf5fL5CSld2qfWvKSqkoKgSgKH8n7/zl9+xeuxqhKJz2s5/Xzi63OZyMOHZqh9pt0rXE1czYmMdigzEXwLaF8MbLsHz74ZkpQ6LdXI+9Od+7GWXTqQQ8Nez/9l0UixWrMwVrUhrWpFS2L3gVn7sGmzMRa0Iy9sRkbEmp2JLSsKdmcnDzGgAyRk7qUHuyBg4CwO/1tnrfXgMGcd3jz/Ljwo/Y8v0SLDY7Uy68jLGnnEFypjnoGk+YQt/VeIKThyefAKddZPTkG4s2NN9jb8r3bkbZdDob//cwCxesaNO+DlXDmRO5a6UlfG4Xe9avQVEtJKa3LdtlWu9cpl95PdOvvL7D7DKJPUyh72pyRhnvy5+F6b8JL9otRcs05Xs3o2w6neLdu7CIAOddfTF+VzWapwa/uwY9EGDEOdcB4K8swVdVireyFF91Bd7qCnw1VWQMHhU22V5r8LpcbPjmS3avXcWeDWvRfD6m/fQqHImxkQjNJDYxhb6rGXA8jL0IljwCPzwLmUPh7ydBsQeGHwP9LHDi8S1HyxwzEYZngTPNmHQlRPibhunK6VDKSspIT5QMmnFN041yO6eY9/pFX/D1S8/gc7tJ79OP0Seewpjpp9N7WNO5mkxMwBT6rkdR4CfPBn31n0NZPlQdBNt+2LIKtjwDqg0emgQVDjhiCiTthx//Z7Qr3ABFG6F4K+jBCVWqHfoeDUdfBe89Ayu3wfQZxjbTldOhlFX6yO6V0OXn9XncfD7vCZJ7ZXHhb/9M7rCRXW6DSffFFPpoIASMPMt41afyIOxfAXuWwq5vgB9hwzKoH5CaOsBw/4yYAb2GGz7/ir2w+WOYX6+O59fPgpYFQwOwvge7cqQ0bo4HVkPJNghocMRMGHRCxIfwFO+jYPlCvJWllHssjMzq+oFKi9VGcmYvKgoLWPXJB5x18x0ooVQaJiYtYAp9LJGSCynnwJHnGMt6AMr3QMAHqhWcGYarJhyn3w8Fa432lfuhbDes+wAuCE6e2dFE5ap4ofIgVOwDfw3YkoynnS2fwsb3oWyX0Uaxgu6HpU/CdZ9D/8kRHXrJo3exdntdBu4+o4/ujG/QLIqqcvmDj5H39mus/vRDbE4np19/S5fbYdI9MYU+llFUyBgcYVsF+ow3XiHO+DM8OhEuKIWxN8LogXX+/HhBSvj6L7D4b4dvUyww+EQ44XYYNA3SBho3wcfHwY6vIhb64uJyspMl0y68GIvNQb9Tftqx3yFCnEnJTLv0KlZ/+iFrv1hgCr1JxJhCH8+oFjj/UXh3Dqz7m/FKzIaT58KkVmQhDPiNJ4WCdVC4Hoo2Qcl28FQYTxkDj4ej/w9yj+q87xKOkh3w/T9h5Usw7hJj3MOWCD6X0XMfMAUSGibqonir8d534mGHa4qyap1hA9OaH4DtIqwOB736D6R4726qS0tIyoiNalQmsY0p9PHO0FPgV1vh4GrYtxLWvQWf/Nrw8af0aX7fDfMh71+wfxXIYLoGoUDmMMgcboho1UH48b9GuOio2Ybop/QJvvpCYpbxZNIRSGm4Yja8Z9xwSrYDAo69CWY8GNmTSskO493vDru5IO8DFr8yD6GARVXRvC7cmp1e/cJl4Y4OVqeT5MwsEtPaFjtv0vMwhb4noChGD7bvRBhxBjw+HhbcY/Tqk3IMQXakNNyncAO8dRVkHQFTfwEZQyBnNGSPAquzYVtPBXz/BCx9CjbOb3RuCyTnGvsPP93odbd0g2mKL/4A3z0OyX2MKKOJV8Pon0Bq38iPMfYiWP0feOdn0GcFpDXMPbNjySfsLYXcVB2PJ4DmCzA43cuYS25rm82dQEpmFhWFBeh6ANWs3WoSAabQ9zTSB8HkOfDDM0bvOETOWMOfPfRkQ/y+f8LovV/9MSS2EGXiSIVTfgsn3wuuEsMPXnnAGBytPGC8CtfDwt/C57+Ho34KMx5qeHPxe4z2SVnG8Rqz4yvI+7fhopn9tHHzaguJmUbOoX9Ngu1fHObC8tTUADD7z0+TkB2bBXCOOGE6W/KWsOLD9zj2/IujbY5JN8AU+p7IzL/BCb80XB81RVC8HfKXwPLnjIiUEJOua1nk6yOE0T6xV3h/fcmO4Dn+bbh9zvgzuMvhkzth8ydGxAxAzhjoM8GInPFVg6sUdn8HGUPhzL+0XeRDZAwFhHEDasSYGRezdvNTfPPwLznrb2/H5MD1sEnHMmDseNZ//bkp9CYRYQp9TyUl13jVcjf4amD/SqgqMNwrA47v2HNmDjV86SU7DDdP2W7Yuwyqi+CoSw3/flWBcdPZusBwEdmSwJoAJ94F0+443G3UFhTFuJls+gim/arBMTPGTWNo35fYuMfLgP/8mdFXNi6D3HXs3bCWrcu+Z83CT8gZOoyJM89DtVipLC6iaOd20nLb6AIz6XEI2bhQdQwwadIkuWJF2xJHmXQDqgrgq/th12LDlXTaHw2fe1ey5VP436WQMYQ9uRewbdMuDh0ooKBcEpAKAsm0E0ZyzK3/6Fq7AKnrvPSrn1N6YB9WuwO/13NYm6xBQzjr5jvIGjCoy+0ziU2EECullGHTo5o9epOuJ7k3nPdky+06k5FnweXv4Ft4P2+9vTS4UhAqc9zvyNGU2fuz/MN3OWLqiV1aK/Xg9q2UHthHUmYvrntsHhabDb/PS0XBQXRdJyE1jcS09HYnSDPpOURSStABLAbswfZvSyn/IIQYj1E+0AFowM+llD+E2T8fqAICgNbUHcfEpMsZfhq2YadyycQFbN2wg6I9e9GDBdnd1TWUrFiGq6KcJf99iZHHT+Pkq+eQkBJmoLiD8VRXAXD2L36NxWYDwGqz08vsvZu0kUh69F7gFClltRDCCnwrhPgU+BPwRynlp0KImcDfgOlNHONkKWVxh1hsYtKRCEG/486i33HhN5cVHGDtFwv4ccFHlOzdzQX33t/p8etrv1yAIzHJdMuYdBgthi9Ig+rgojX4ksFXKD4uFTg8hMHEpJuT3rsPJ11xLbPv+j1lBw/w0h03se6rhUhd77RzHtq9i0HjJ2JPSOy0c5j0LCKKUxNCqEKIH4Ei4HMp5TLgduBhIcRe4O/A3CZ2l8BCIcRKIcScZs4xRwixQgix4tChQ635DiYmnc7AceO54qHHyew/kIXP/JNnfn41X77wFMV7d3foefZuXEfloSJyBndOTnuTnkmrom6EEGnAe8CtwBzgGynlO0KIi4E5UsrTwuzTR0p5QAiRDXwO3CqlXNzcecyoG5NYReo6W5d9z5bvF7Prx5UIIbj6H0+R0iur/ceWkvf//mf2bljHTfNerfXPm5hEQodF3Ugpy4UQi4AZwFVAaF74W8BzTexzIPheJIR4D5iMMbhrYtLtEIrCyCknMHLKCZQd3M8Lt9/A5u++YfJ5F7a4r6e6mkO7d3Jo9y6K9+3BmZyCPSERV0UZVcXFFO3eSXnBQaZddrUp8iYdSiRRN1mAPyjyTuA04K8YPvmTgEXAKcC2MPsmAoqUsir4+QyMQVwTk25Pem5fsgYNYfWCDzly2vQGIZiuygoKdmylYPtWivJ3UpS/k6riOpekIykZr6sGqetY7HaSM3qR2W8Ak2dfxJjpp0fj65jEMZH06HOBl4UQKoZP/00p5UdCiHLgcSGEBfBguHIQQvQBnpNSzgRygPeC8b4W4DUp5YKO/xomJtFhygWX8uGjDzHvpqtJSE1DDwQI+P21k5yEUEjv05e+I0eRdcZgsgcOJmvQEBLT0glofgJ+P1aH04yJN+lUzJmxJibtpOzgfrZ8v4SqkmIUiwXVaiUxLZ3coSPIHjIUm6MD0jaYmLSAOTPWxKQTSc/ty3EXXBptM0xMmsRMZm1iYmIS55hCb2JiYhLnmEJvYmJiEueYQm9iYmIS55hCb2JiYhLnmEJvYmJiEueYQm9iYmIS55hCb2JiYhLnxOTMWCHEIWA30AswC5aEx7w2TWNem/CY16Vp4uHaDJRShk2jGpNCH0IIscIsPRge89o0jXltwmNel6aJ92tjum5MTExM4hxT6E1MTEzinFgX+nnRNiCGMa9N05jXJjzmdWmauL42Me2jNzExMTFpP7HeozcxMTExaScxKfRCiPFCiKVCiB+FECuEEJOD6wcJIdzB9T8KIZ6Otq1dSVPXpd72AUKIaiHEndGyMVo085uZXO/3skYIcX60be1qmrk2pwshVgoh1gXfT4m2rV1NM9cmUwjxdfDv6V/RtrPdSClj7gUsBM4Kfp4JLAp+HgSsj7Z9sXZd6m1/B6NQ+53RtjVWrg2QAFiCn3OBotByT3k1c20mAH2Cn8cA+6Ntawxdm0TgBOBG4F/RtrO9r1itMCWBlODnVIxC5CbNXBchxGxgJ1DT9WbFBGGvjZTSVa+NI9iup9HUtVldr80GwCGEsEspvV1sXzRp6trUAN8KIYZFy7COJCYHY4UQRwKfAQLDvXS8lHK3EGIQxg9yK1AJ/FZKuSRqhnYxzVyXROAL4HTgTqBaSvn36Fna9TR1bYLbjgVeAAYC/yelfC9qhkaB5q5NvTYXAjdKKU+LgolRo6VrI4S4GpgkpbwlOhZ2DFHr0QshvgB6h9l0L3Aq8Esp5TtCiIuB54HTgIPAAClliRBiIjBfCDFaSlnZZYZ3Mm28Ln8EHpVSVgshus7YLqaN1wYp5TJgdPCP+mUhxKdSSk9X2d0VtPXaBPcdDfwVOKMrbO1q2nNt4oVY7dFXAGlSSikM5aqQUqaEabcIwx+9oqttjAZNXRchxBKgf7BZGqADv5dSdv9BpAhpxW/ma+DXPeU3A81fGyFEP+Ar4Bop5XfRtDMatPS7iZcefUxG3WD4yU4Kfj4F2AYghMgSQqjBz0OA4Rh+6Z5C2OsipZwmpRwkpRwEPAb8pSeJfJCmfjODhRCW4OeBwEggPxoGRpGmrk0a8DEwtyeKfJCw1ybeiNXB2OuBx4N/oB5gTnD9icCfhBAaEMDwKZZGycZo0NR1MWn62pwA3COE8GM86fxcStndsxS2lqauzS3AMOB3QojfBdedIaUsioKN0aLJvykhRD7GQK0tGOxwhpRyYzSMbC8x6boxMTExMek4YtV1Y2JiYmLSQZhCb2JiYhLnmEJvYmJiEueYQm9iYmIS55hCb2JiYhLnmEJvYmJiEueYQm9iYmIS55hCb2JiYhLn/D8d5KFuWB+0ngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minRelDevn = 1.5\n",
    "print(\"Here is where pct w Bach degree is\",minRelDevn,\"less (red) or more (blue) than HD expectation\")\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "for t in range(nTracts):\n",
    "    if relDevnPctBach[t] > minRelDevn:\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"blue\")\n",
    "    if relDevnPctBach[t] < -1. * minRelDevn:        \n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker=\".\",color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MO 20AugGOPwins\n"
     ]
    }
   ],
   "source": [
    "date = \"20AugGOPwins\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "districtCPx = [0.]*nDistricts\n",
    "districtCPy = [0.]*nDistricts\n",
    "districtNo = [0.]*nDistricts\n",
    "for d in range(nDistricts):\n",
    "    districtCPx[d]=enactedGeom[d].centroid.x\n",
    "    districtCPy[d]=enactedGeom[d].centroid.y\n",
    "    districtNo[d]=d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the red-blue of the enacted map\n",
    "for d in range(nDistricts):\n",
    "    redd = min(max( 0, ( EDvGOP[d] - 0.5) * 3.0 ),1)\n",
    "    bluu = min(max( 0, (0.5 - EDvGOP[d]) * 3.0 ),1)\n",
    "    plt.text(districtCPx[d],districtCPy[d],round(d+1+EDvGOP[d],3),color=(redd, 0,bluu ) )\n",
    "    plotPoly(enactedGeom[d])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e2e3e9eb-e786-4f98-b1aa-5c57af7150a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#SKIP THIS - analyzing Ohio legislative\n",
    "nDistricts = 99\n",
    "OHfile = pd.read_csv(\"Johnson2.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39f93566-3185-455f-8512-602347708e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USUALLY SKIP THIS -- USED FOR IMPORTING RED/BLUE LEAN FILE INSTEAD OF CALCULATING\n",
    "demVote = OHfile[\"pctDem\"]\n",
    "gopVote = OHfile[\"pctRep\"]\n",
    "nDistricts = len(demVote)\n",
    "EDvGOP = [0.]*nDistricts\n",
    "for d in range(nDistricts):\n",
    "    EDvGOP = gopVote / (demVote + gopVote)\n",
    "EDvGOP = EDvGOP.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted district pct GOP by district for NJ\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED red-blue lean in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted district pct GOP by district for\",STATE) \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvGOP, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED home district pct Hispanic in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvHisp, n_bins) #bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts is 6.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy enacted districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for d in range(nDistricts):\n",
    "    if (EDvHisp[d] > minHisp1) :\n",
    "        sumHispDistricts += 1\n",
    "        if (EDvHisp[d] < minHisp2):\n",
    "            plt.scatter(districtCPx[d],districtCPy[d],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(districtCPx[d],districtCPy[d],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts is\",sumHispDistricts)\n",
    "for d in range(nDistricts):\n",
    "    plotPoly(enactedGeom[d])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of enacted VAP pct Black by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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SQ0l+Ncn/VtUHkryxu5/fqrlHNOelt25J8mtJ/nHy++pxV5Cebs79/OOsXmvzZ0leSvIna06aYI0595M5Oc0cgCH5iA+AIQkUAEMSKACGJFAADEmgABiSQAEwJIECYEj/B8vU+YVlmSBkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE ENACTED home district pct Black in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of enacted VAP pct Black by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(EDvBlack, n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 3.0 OH seats out of 15\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #UPDATED FOR ENACTED\n",
    "isOppor = [0]*nDistricts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nDistricts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-EDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * EDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if EDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += 1./float(nDistricts)\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = enactedMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the FL map with Hisp+Black greater than  0.4 or even 0.5\n",
      "                        1 light opportunity districts and  8 OPPOR districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy enacted districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "counter=0\n",
    "counter2=0\n",
    "for t in range(nDistricts):\n",
    "    if (EDvBlack[t] + EDvHisp[t]) > minMinor:\n",
    "        if (EDvBlack[t] + EDvHisp[t]) > minMinor2 :\n",
    "            plt.text(districtCPx[t],districtCPy[t],t+1,color='blue' )\n",
    "            plotPoly(enactedGeom[t])\n",
    "            counter +=1\n",
    "            counter2 +=1\n",
    "        else :            \n",
    "            plt.scatter(districtCPx[t],districtCPy[t],marker='.',color='gray' )\n",
    "            counter2 += 1\n",
    "print(\"                       \",counter2-counter,\"light opportunity districts and \",counter,\"OPPOR districts\")\n",
    "plotPoly(enactedMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of ED area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(\n",
    "    EDarea,EDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"district area\", ylabel=\"enacted district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 100  #normally 1000 for HD code, but we have <100 districts ...\n",
    "dV = 1./nBins\n",
    "binDistricts = [0]*nBins\n",
    "\n",
    "for d in range(nDistricts):\n",
    "    binNo = int(EDvGOP[d]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binDistricts[binNo] += 1  \n",
    "\n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binVote, binDistricts,width = 0.01)\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"number of districts\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "#we will differ here from the HD code; do a simple stair-step instead\n",
    "stepStart = [0.]*(nDistricts+1)  #this is the minimum vote for the GOP to win x number of seats, not x-1\n",
    "stepStop = [0.]*(nDistricts+1)   #this is the max vote for the GOP to win x, not x+1 seats\n",
    "sortedEDvGOP = [0.]*nDistricts\n",
    "sortedEDvGOP = EDvGOP\n",
    "sortedEDvGOP.sort(reverse=True)  #ordering the districts from most GOP to least to enable stair-step plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for d in range(nDistricts+1):\n",
    "    if d == 0:\n",
    "        stepStart[d] = 0.\n",
    "    else:\n",
    "        stepStart[d] = min( 1,max(0,stateGOP + 0.5 - sortedEDvGOP[d-1]) )\n",
    "    if d == nDistricts:\n",
    "        stepStop[d] = 1.\n",
    "    else:\n",
    "        stepStop[d] = min( 1,max(0,stateGOP + 0.5 - sortedEDvGOP[d]) )\n",
    "    xValues = [stepStart[d],stepStop[d]]\n",
    "    yValues = [d,d]\n",
    "    plt.plot(xValues,yValues)\n",
    "\n",
    "ax.set(xlabel=\"statewide vote, \"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "districtRANGE = [0.01*nDistricts, 0.99*nDistricts]\n",
    "districtFifty50 = [0.5*nDistricts, 0.5*nDistricts]\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "#plt.plot(RANGE,districtFifty50)\n",
    "#plt.plot(fifty50,districtRANGE)\n",
    "plt.text(stateGOP,0.5*nDistricts,\"+\",ha='center',va='center',fontsize=20)\n",
    "plt.text(stateGOP +0.02,0.05*nDistricts,\"statewide vote =\"+str(round(stateGOP,3)),color='red')\n",
    "plt.plot(expected,districtRANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#DO NOT COMPUTE responsiveness in absence of fractional seat uncertainty.  See HD code for an idea here\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.  About 20sec to run with 10K bins\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000  #could easily push to 10000, but little gain in accuracy\n",
    "dV = 1./float(nBins)\n",
    "nSigma = 8.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "binWeight = [0.]*nBins\n",
    "binDistricts = [0]*nBins  #how many districts have an expected vote in this bin\n",
    "\n",
    "for d in range(nDistricts):\n",
    "    binNo = int(EDvGOP[d]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binDistricts[binNo] += 1  #one more bin goes in this district\n",
    "    binWeight[binNo] = binDistricts[binNo]/float(nDistricts) #update fraction of districts in this bin\n",
    "\n",
    "binVote = [0.]*nBins  \n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV  #this will store the statewide vote for this bin\n",
    "\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)  #left tail weight\n",
    "highSliverWt = lowSliverWt                #right tail weight = left tail weight\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )  #debug check\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    leftBinNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if leftBinNo < 0:\n",
    "        leftBinNo = 0\n",
    "    smearedBinWeight[leftBinNo] += binWeight[b]*lowSliverWt\n",
    "    rightBinNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if rightBinNo >= nBins:\n",
    "        rightBinNo = nBins -1 \n",
    "    smearedBinWeight[rightBinNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared \"+str(seatVar))\n",
    "RANGE = [0.0, 0.5*np.max(smearedBinWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "\n",
    "#NOW, CONVERT THE UNSMEARED VOTE TO AN S(V) CURVE - and find the # of GOP seats at the statewide vote\n",
    "cumSeats = [0.]*(nDistricts+1)\n",
    "for d in range(nDistricts+1):\n",
    "    cumSeats[d] = float(d)/float(nDistricts)\n",
    "    xValues = [stepStart[d],stepStop[d]]\n",
    "    yValues = [cumSeats[d],cumSeats[d]]\n",
    "    plt.plot(xValues,yValues)\n",
    "    if stateGOP > stepStart[d] and stateGOP <= stepStop[d] : #is the statewide vote in this step?\n",
    "        expectedSeats = float(d)/nDistricts\n",
    "\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"frac GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\" exp seatFrac, no smear\")\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\" exp seatFrac, smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fractional-seat-smeared (var of 0.04 ) responsiveness is 1.928\n",
      "fractional expected GOP seats =  6.765  out of  8 seats for MO\n",
      "simpler expected GOP seats =  7.0  out of  8 seats\n",
      "to repeat from earlier block, non-smeared and smeared fraction of seats = 0.875 0.846\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's  ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE\n",
    "#unlike Home District code, we do not attempt responsiveness for unsmeared -- too stair-steppy\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "#print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional-seat-smeared (var of\",seatVar,\") responsiveness is\",round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n",
    "print(\"to repeat from earlier block, non-smeared and smeared fraction of seats =\"\n",
    "      ,round(expectedSeats,3),round(smearedSeats,3) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save these fabulous results to a file\n",
    "params = [STATE+\"-\"+str(nDistricts)+\"d\",\"totalGOPseats\",round(nDistricts*smearedSeats,3),\"fracGOPseats\",\n",
    "          smearedSeats,\"nTracts\",nTracts]\n",
    "for p in range(nDistricts - len(params) ):\n",
    "    params.append(\"blank\")\n",
    "\n",
    "df = pd.DataFrame( {\"parameters\":params,\"seatPctGOP\":EDvGOP} ) \n",
    "\n",
    "outname = STATE+\"enacted.csv\"\n",
    "outpath = \"enacted_analysis/\"+outname\n",
    "df.to_csv(outpath)\n",
    "#SOMEHOW, THIS IS A SORTED LIST, NOT SURE WHERE IT GOT SORTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "0af834a7-8b87-4bb9-92b8-d400fdcc1ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6122725899841236,\n",
       " 0.5256773381854366,\n",
       " 0.47959749788904144,\n",
       " 0.4372265645587057,\n",
       " 0.42723460256808093,\n",
       " 0.41303605758650064,\n",
       " 0.4063293882649418,\n",
       " 0.4024856658179458,\n",
       " 0.3762191064630529,\n",
       " 0.3250965133559313,\n",
       " 0.2743920436164206,\n",
       " 0.18735677903675224]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EDvGOP  #confirming that this got sorted somehow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f4d302f-1bb7-4f40-868b-9f19c4568e62",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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